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Add new MuJoCo bindings (#2762)

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This commit is contained in:
Rodrigo de Lazcano
2022-05-24 08:47:51 -04:00
committed by GitHub
parent 8f9b62f6a6
commit 3e006f3ea5
43 changed files with 3329 additions and 1523 deletions
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1
.github/workflows/build.yml vendored
View File

@@ -11,7 +11,6 @@ jobs:
- uses: actions/checkout@v2
- run: |
docker build -f py.Dockerfile \
--build-arg MUJOCO_KEY=$MUJOCO_KEY \
--build-arg PYTHON_VERSION=${{ matrix.python-version }} \
--tag gym-docker .
- name: Run tests

2
.pre-commit-config.yaml
View File

@@ -9,7 +9,7 @@ repos:
hooks:
- id: codespell
args:
- --ignore-words-list=nd,reacher,thist,ths
- --ignore-words-list=nd,reacher,thist,ths, ure, referenc
- repo: https://gitlab.com/PyCQA/flake8
rev: 4.0.1
hooks:

5
README.md
View File

@@ -46,6 +46,11 @@ env.close()
Gym keeps strict versioning for reproducibility reasons. All environments end in a suffix like "\_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.
## MuJoCo Environments
The latest "\_v4" and future versions of the MuJoCo environments will no longer depend on `mujoco-py`. Instead `mujoco` will be the required dependency for future gym MuJoCo environment versions. Old gym MuJoCo environment versions that depend on `mujoco-py` will still be kept but unmaintained.
To install the dependencies for the latest gym MuJoCo environments use `pip install gym[mujoco]`. Dependencies for old MuJoCo environments can still be installed by `pip install gym[mujoco_py]`.
## Citation
A whitepaper from when Gym just came out is available https://arxiv.org/pdf/1606.01540, and can be cited with the following bibtex entry:

74
gym/envs/__init__.py
View File

@@ -162,6 +162,13 @@ register(
reward_threshold=-3.75,
)
register(
id="Reacher-v4",
entry_point="gym.envs.mujoco.reacher_v4:ReacherEnv",
max_episode_steps=50,
reward_threshold=-3.75,
)
register(
id="Pusher-v2",
entry_point="gym.envs.mujoco:PusherEnv",
@@ -169,6 +176,13 @@ register(
reward_threshold=0.0,
)
register(
id="Pusher-v4",
entry_point="gym.envs.mujoco.pusher_v4:PusherEnv",
max_episode_steps=100,
reward_threshold=0.0,
)
register(
id="InvertedPendulum-v2",
entry_point="gym.envs.mujoco:InvertedPendulumEnv",
@@ -176,6 +190,13 @@ register(
reward_threshold=950.0,
)
register(
id="InvertedPendulum-v4",
entry_point="gym.envs.mujoco.inverted_pendulum_v4:InvertedPendulumEnv",
max_episode_steps=1000,
reward_threshold=950.0,
)
register(
id="InvertedDoublePendulum-v2",
entry_point="gym.envs.mujoco:InvertedDoublePendulumEnv",
@@ -183,6 +204,13 @@ register(
reward_threshold=9100.0,
)
register(
id="InvertedDoublePendulum-v4",
entry_point="gym.envs.mujoco.inverted_double_pendulum_v4:InvertedDoublePendulumEnv",
max_episode_steps=1000,
reward_threshold=9100.0,
)
register(
id="HalfCheetah-v2",
entry_point="gym.envs.mujoco:HalfCheetahEnv",
@@ -197,6 +225,13 @@ register(
reward_threshold=4800.0,
)
register(
id="HalfCheetah-v4",
entry_point="gym.envs.mujoco.half_cheetah_v4:HalfCheetahEnv",
max_episode_steps=1000,
reward_threshold=4800.0,
)
register(
id="Hopper-v2",
entry_point="gym.envs.mujoco:HopperEnv",
@@ -211,6 +246,13 @@ register(
reward_threshold=3800.0,
)
register(
id="Hopper-v4",
entry_point="gym.envs.mujoco.hopper_v4:HopperEnv",
max_episode_steps=1000,
reward_threshold=3800.0,
)
register(
id="Swimmer-v2",
entry_point="gym.envs.mujoco:SwimmerEnv",
@@ -225,6 +267,13 @@ register(
reward_threshold=360.0,
)
register(
id="Swimmer-v4",
entry_point="gym.envs.mujoco.swimmer_v4:SwimmerEnv",
max_episode_steps=1000,
reward_threshold=360.0,
)
register(
id="Walker2d-v2",
max_episode_steps=1000,
@@ -237,6 +286,12 @@ register(
entry_point="gym.envs.mujoco.walker2d_v3:Walker2dEnv",
)
register(
id="Walker2d-v4",
max_episode_steps=1000,
entry_point="gym.envs.mujoco.walker2d_v4:Walker2dEnv",
)
register(
id="Ant-v2",
entry_point="gym.envs.mujoco:AntEnv",
@@ -251,6 +306,13 @@ register(
reward_threshold=6000.0,
)
register(
id="Ant-v4",
entry_point="gym.envs.mujoco.ant_v4:AntEnv",
max_episode_steps=1000,
reward_threshold=6000.0,
)
register(
id="Humanoid-v2",
entry_point="gym.envs.mujoco:HumanoidEnv",
@@ -263,8 +325,20 @@ register(
max_episode_steps=1000,
)
register(
id="Humanoid-v4",
entry_point="gym.envs.mujoco.humanoid_v4:HumanoidEnv",
max_episode_steps=1000,
)
register(
id="HumanoidStandup-v2",
entry_point="gym.envs.mujoco:HumanoidStandupEnv",
max_episode_steps=1000,
)
register(
id="HumanoidStandup-v4",
entry_point="gym.envs.mujoco.humanoidstandup_v4:HumanoidStandupEnv",
max_episode_steps=1000,
)

1
gym/envs/mujoco/__init__.py
View File

@@ -9,6 +9,7 @@ from gym.envs.mujoco.humanoid import HumanoidEnv
from gym.envs.mujoco.humanoidstandup import HumanoidStandupEnv
from gym.envs.mujoco.inverted_double_pendulum import InvertedDoublePendulumEnv
from gym.envs.mujoco.inverted_pendulum import InvertedPendulumEnv
from gym.envs.mujoco.mujoco_rendering import RenderContextOffscreen, Viewer
from gym.envs.mujoco.pusher import PusherEnv
from gym.envs.mujoco.reacher import ReacherEnv
from gym.envs.mujoco.swimmer import SwimmerEnv

2
gym/envs/mujoco/ant.py
View File

@@ -6,7 +6,7 @@ from gym.envs.mujoco import mujoco_env
class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "ant.xml", 5)
mujoco_env.MujocoEnv.__init__(self, "ant.xml", 5, mujoco_bindings="mujoco_py")
utils.EzPickle.__init__(self)
def step(self, a):

163
gym/envs/mujoco/ant_v3.py
View File

@@ -9,167 +9,6 @@ DEFAULT_CAMERA_CONFIG = {
class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Schulman,
Moritz, Levine, Jordan and Abbeel in ["High-Dimensional Continuous Control
Using Generalized Advantage Estimation"](https://arxiv.org/abs/1506.02438).
The ant is a 3D robot consisting of one torso (free rotational body) with
four legs attached to it with each leg having two links. The goal is to
coordinate the four legs to move in the forward (right) direction by applying
torques on the eight hinges connecting the two links of each leg and the torso
(nine parts and eight hinges).
### Action Space
The action space is a `Box(-1, 1, (8,), float32)`. An action represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the rotor between the torso and front left hip | -1 | 1 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the rotor between the front left two links | -1 | 1 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the rotor between the torso and front right hip | -1 | 1 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between the front right two links | -1 | 1 | angle_2 (front_right_leg) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between the torso and back left hip | -1 | 1 | hip_3 (back_leg) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between the back left two links | -1 | 1 | angle_3 (back_leg) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the torso and back right hip | -1 | 1 | hip_4 (right_back_leg) | hinge | torque (N m) |
| 7 | Torque applied on the rotor between the back right two links | -1 | 1 | angle_4 (right_back_leg) | hinge | torque (N m) |
### Observation Space
Observations consist of positional values of different body parts of the ant,
followed by the velocities of those individual parts (their derivatives) with all
the positions ordered before all the velocities.
By default, observations do not include the x- and y-coordinates of the ant's torso. These may
be included by passing `exclude_current_positions_from_observation=False` during construction.
In that case, the observation space will have 113 dimensions where the first two dimensions
represent the x- and y- coordinates of the ant's torso.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
of the torso will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
However, by default, an observation is a `ndarray` with shape `(111,)`
where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|-------------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | z-coordinate of the torso (centre) | -Inf | Inf | torso | free | position (m) |
| 1 | x-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 2 | y-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 3 | z-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 4 | w-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 5 | angle between torso and first link on front left | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) |
| 6 | angle between the two links on the front left | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) |
| 7 | angle between torso and first link on front right | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) |
| 8 | angle between the two links on the front right | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) |
| 9 | angle between torso and first link on back left | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) |
| 10 | angle between the two links on the back left | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) |
| 11 | angle between torso and first link on back right | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) |
| 12 | angle between the two links on the back right | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) |
| 13 | x-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 14 | y-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 15 | z-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 16 | x-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 17 | y-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 18 | z-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 19 | angular velocity of angle between torso and front left link | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) |
| 20 | angular velocity of the angle between front left links | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) |
| 21 | angular velocity of angle between torso and front right link| -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) |
| 22 | angular velocity of the angle between front right links | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) |
| 23 | angular velocity of angle between torso and back left link | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) |
| 24 | angular velocity of the angle between back left links | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) |
| 25 | angular velocity of angle between torso and back right link | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) |
| 26 |angular velocity of the angle between back right links | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) |
The remaining 14*6 = 84 elements of the observation are contact forces
(external forces - force x, y, z and torque x, y, z) applied to the
center of mass of each of the links. The 14 links are: the ground link,
the torso link, and 3 links for each leg (1 + 1 + 12) with the 6 external forces.
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the [Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There have been reported issues that using a Mujoco-Py version > 2.0 results
in the contact forces always being 0. As such we recommend to use a Mujoco-Py version < 2.0
when using the Ant environment if you would like to report results with contact forces (if
contact forces are not used in your experiments, you can use version > 2.0).
### Rewards
The reward consists of three parts:
- *healthy_reward*: Every timestep that the ant is healthy (see definition in section "Episode Termination"), it gets a reward of fixed value `healthy_reward`
- *forward_reward*: A reward of moving forward which is measured as
*(x-coordinate before action - x-coordinate after action)/dt*. *dt* is the time
between actions and is dependent on the `frame_skip` parameter (default is 5),
where the frametime is 0.01 - making the default *dt = 5 * 0.01 = 0.05*.
This reward would be positive if the ant moves forward (in positive x direction).
- *ctrl_cost*: A negative reward for penalising the ant if it takes actions
that are too large. It is measured as *`ctrl_cost_weight` * sum(action<sup>2</sup>)*
where *`ctr_cost_weight`* is a parameter set for the control and has a default value of 0.5.
- *contact_cost*: A negative reward for penalising the ant if the external contact
force is too large. It is calculated *`contact_cost_weight` * sum(clip(external contact
force to `contact_force_range`)<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost* and `info` will also contain the individual reward terms.
### Starting State
All observations start in state
(0.0, 0.0, 0.75, 1.0, 0.0 ... 0.0) with a uniform noise in the range
of [-`reset_noise_scale`, `reset_noise_scale`] added to the positional values and standard normal noise
with mean 0 and standard deviation `reset_noise_scale` added to the velocity values for
stochasticity. Note that the initial z coordinate is intentionally selected
to be slightly high, thereby indicating a standing up ant. The initial orientation
is designed to make it face forward as well.
### Episode Termination
The ant is said to be unhealthy if any of the following happens:
1. Any of the state space values is no longer finite
2. The z-coordinate of the torso is **not** in the closed interval given by `healthy_z_range` (defaults to [0.2, 1.0])
If `terminate_when_unhealthy=True` is passed during construction (which is the default),
the episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. The ant is unhealthy
If `terminate_when_unhealthy=False` is passed, the episode is terminated only when 1000 timesteps are exceeded.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
env = gym.make('Ant-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Ant-v3', ctrl_cost_weight=0.1, ...)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|--------------|-------------------------------|
| `xml_file` | **str** | `"ant.xml"` | Path to a MuJoCo model |
| `ctrl_cost_weight` | **float** | `0.5` | Weight for *ctrl_cost* term (see section on reward) |
| `contact_cost_weight` | **float** | `5e-4` | Weight for *contact_cost* term (see section on reward) |
| `healthy_reward` | **float** | `1` | Constant reward given if the ant is "healthy" after timestep |
| `terminate_when_unhealthy` | **bool**| `True` | If true, issue a done signal if the z-coordinate of the torso is no longer in the `healthy_z_range` |
| `healthy_z_range` | **tuple** | `(0.2, 1)` | The ant is considered healthy if the z-coordinate of the torso is in this range |
| `contact_force_range` | **tuple** | `(-1, 1)` | Contact forces are clipped to this range in the computation of *contact_cost* |
| `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True`| Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="ant.xml",
@@ -199,7 +38,7 @@ class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5, mujoco_bindings="mujoco_py")
@property
def healthy_reward(self):

308
gym/envs/mujoco/ant_v4.py Normal file
View File

@@ -0,0 +1,308 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
"distance": 4.0,
}
class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Schulman,
Moritz, Levine, Jordan and Abbeel in ["High-Dimensional Continuous Control
Using Generalized Advantage Estimation"](https://arxiv.org/abs/1506.02438).
The ant is a 3D robot consisting of one torso (free rotational body) with
four legs attached to it with each leg having two links. The goal is to
coordinate the four legs to move in the forward (right) direction by applying
torques on the eight hinges connecting the two links of each leg and the torso
(nine parts and eight hinges).
### Action Space
The agent take a 8-element vector for actions.
The action space is a continuous `(action, action, action, action, action, action,
action, action)` all in `[-1, 1]`, where `action` represents the numerical torques
applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the rotor between the torso and front left hip | -1 | 1 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the rotor between the front left two links | -1 | 1 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the rotor between the torso and front right hip | -1 | 1 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between the front right two links | -1 | 1 | angle_2 (front_right_leg) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between the torso and back left hip | -1 | 1 | hip_3 (back_leg) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between the back left two links | -1 | 1 | angle_3 (back_leg) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the torso and back right hip | -1 | 1 | hip_4 (right_back_leg) | hinge | torque (N m) |
| 7 | Torque applied on the rotor between the back right two links | -1 | 1 | angle_4 (right_back_leg) | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the ant,
followed by the velocities of those individual parts (their derivatives) with all
the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(111,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|-------------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | x-coordinate of the torso (centre) | -Inf | Inf | torso | free | position (m) |
| 1 | y-coordinate of the torso (centre) | -Inf | Inf | torso | free | position (m) |
| 2 | z-coordinate of the torso (centre) | -Inf | Inf | torso | free | position (m) |
| 3 | x-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 4 | y-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 5 | z-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 6 | w-orientation of the torso (centre) | -Inf | Inf | torso | free | angle (rad) |
| 7 | angle between torso and first link on front left | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) |
| 8 | angle between the two links on the front left | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) |
| 9 | angle between torso and first link on front right | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) |
| 10 | angle between the two links on the front right | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) |
| 11 | angle between torso and first link on back left | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) |
| 12 | angle between the two links on the back left | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) |
| 13 | angle between torso and first link on back right | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) |
| 14 | angle between the two links on the back right | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) |
| 15 | x-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 16 | y-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 17 | z-coordinate velocity of the torso | -Inf | Inf | torso | free | velocity (m/s) |
| 18 | x-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 19 | y-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 20 | z-coordinate angular velocity of the torso | -Inf | Inf | torso | free | angular velocity (rad/s) |
| 21 | angular velocity of angle between torso and front left link | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) |
| 22 | angular velocity of the angle between front left links | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) |
| 23 | angular velocity of angle between torso and front right link| -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) |
| 24 | angular velocity of the angle between front right links | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) |
| 25 | angular velocity of angle between torso and back left link | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) |
| 26 | angular velocity of the angle between back left links | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) |
| 27 | angular velocity of angle between torso and back right link | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) |
| 28 |angular velocity of the angle between back right links | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) |
The remaining 14*6 = 84 elements in the state are contact forces
(external forces - force x, y, z and torque x, y, z) applied to the
center of mass of each of the links. The 14 links are: the ground link,
the torso link, and 3 links for each leg (1 + 1 + 12) with the 6 external forces.
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the [Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There are 29 elements in the table above - giving rise to `(113,)` elements
in the state space. In practice (and Gym implementation), the first two positional
elements are omitted from the state space since the reward function is calculated based
on the x-coordinate value. This value is hidden from the algorithm, which in turn has to
develop an abstract understanding of it from the observed rewards. Therefore, observation
space has shape `(111,)` instead of `(113,)` and the table should not have the first two rows.
**Note:** Ant-v4 environment no longer has the following contact forces issue.
If using previous Ant versions from v4, there have been reported issues that using a Mujoco-Py version > 2.0 results
in the contact forces always being 0. As such we recommend to use a Mujoco-Py version < 2.0
when using the Ant environment if you would like to report results with contact forces (if
contact forces are not used in your experiments, you can use version > 2.0).
**Note:** Ant-v4 has the option of including contact forces in the observation space. To add contact forces set the argument
'use_contact_forces" to True. The default value is False. Also note that training including contact forces can perform worse
than not using them as shown in (https://github.com/openai/gym/pull/2762).
### Rewards
The reward consists of three parts:
- *survive_reward*: Every timestep that the ant is alive, it gets a reward of 1.
- *forward_reward*: A reward of moving forward which is measured as
*(x-coordinate before action - x-coordinate after action)/dt*. *dt* is the time
between actions and is dependent on the frame_skip parameter (default is 5),
where the *dt* for one frame is 0.01 - making the default *dt = 5 * 0.01 = 0.05*.
This reward would be positive if the ant moves forward (right) desired.
- *ctrl_cost*: A negative reward for penalising the ant if it takes actions
that are too large. It is measured as *coefficient **x** sum(action<sup>2</sup>)*
where *coefficient* is a parameter set for the control and has a default value of 0.5.
- *contact_cost*: A negative reward for penalising the ant if the external contact
force is too large. It is calculated *0.5 * 0.001 * sum(clip(external contact
force to [-1,1])<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *alive survive_reward + forward_reward - ctrl_cost - contact_cost*
### Starting State
All observations start in state
(0.0, 0.0, 0.75, 1.0, 0.0 ... 0.0) with a uniform noise in the range
of [-0.1, 0.1] added to the positional values and standard normal noise
with 0 mean and 0.1 standard deviation added to the velocity values for
stochasticity. Note that the initial z coordinate is intentionally selected
to be slightly high, thereby indicating a standing up ant. The initial orientation
is designed to make it face forward as well.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
3. The y-orientation (index 2) in the state is **not** in the range `[0.2, 1.0]`
### Arguments
No additional arguments are currently supported (in v2 and lower), but modifications
can be made to the XML file in the assets folder (or by changing the path to a modified
XML file in another folder).
```
env = gym.make('Ant-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Ant-v3', ctrl_cost_weight=0.1, ...)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="ant.xml",
ctrl_cost_weight=0.5,
use_contact_forces=False,
contact_cost_weight=5e-4,
healthy_reward=1.0,
terminate_when_unhealthy=True,
healthy_z_range=(0.2, 1.0),
contact_force_range=(-1.0, 1.0),
reset_noise_scale=0.1,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._ctrl_cost_weight = ctrl_cost_weight
self._contact_cost_weight = contact_cost_weight
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_z_range = healthy_z_range
self._contact_force_range = contact_force_range
self._reset_noise_scale = reset_noise_scale
self._use_contact_forces = use_contact_forces
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
@property
def healthy_reward(self):
return (
float(self.is_healthy or self._terminate_when_unhealthy)
* self._healthy_reward
)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
@property
def contact_forces(self):
raw_contact_forces = self.data.cfrc_ext
min_value, max_value = self._contact_force_range
contact_forces = np.clip(raw_contact_forces, min_value, max_value)
return contact_forces
@property
def contact_cost(self):
contact_cost = self._contact_cost_weight * np.sum(
np.square(self.contact_forces)
)
return contact_cost
@property
def is_healthy(self):
state = self.state_vector()
min_z, max_z = self._healthy_z_range
is_healthy = np.isfinite(state).all() and min_z <= state[2] <= max_z
return is_healthy
@property
def done(self):
done = not self.is_healthy if self._terminate_when_unhealthy else False
return done
def step(self, action):
xy_position_before = self.get_body_com("torso")[:2].copy()
self.do_simulation(action, self.frame_skip)
xy_position_after = self.get_body_com("torso")[:2].copy()
xy_velocity = (xy_position_after - xy_position_before) / self.dt
x_velocity, y_velocity = xy_velocity
forward_reward = x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
costs = ctrl_cost = self.control_cost(action)
done = self.done
observation = self._get_obs()
info = {
"reward_forward": forward_reward,
"reward_ctrl": -ctrl_cost,
"reward_survive": healthy_reward,
"x_position": xy_position_after[0],
"y_position": xy_position_after[1],
"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
"x_velocity": x_velocity,
"y_velocity": y_velocity,
"forward_reward": forward_reward,
}
if self._use_contact_forces:
contact_cost = self.contact_cost
costs += contact_cost
info["reward_ctrl"] = -contact_cost
reward = rewards - costs
return observation, reward, done, info
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = self.data.qvel.flat.copy()
if self._exclude_current_positions_from_observation:
position = position[2:]
if self._use_contact_forces:
contact_force = self.contact_forces.flat.copy()
return np.concatenate((position, velocity, contact_force))
else:
return np.concatenate((position, velocity))
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = (
self.init_qvel
+ self._reset_noise_scale * self.np_random.standard_normal(self.model.nv)
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

2
gym/envs/mujoco/assets/inverted_pendulum.xml
View File

@@ -22,6 +22,6 @@
</body>
</worldbody>
<actuator>
<motor gear="100" joint="slider" name="slide"/>
<motor ctrllimited="true" ctrlrange="-3 3" gear="100" joint="slider" name="slide"/>
</actuator>
</mujoco>

4
gym/envs/mujoco/half_cheetah.py
View File

@@ -6,7 +6,9 @@ from gym.envs.mujoco import mujoco_env
class HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "half_cheetah.xml", 5)
mujoco_env.MujocoEnv.__init__(
self, "half_cheetah.xml", 5, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def step(self, action):

121
gym/envs/mujoco/half_cheetah_v3.py
View File

@@ -10,125 +10,6 @@ DEFAULT_CAMERA_CONFIG = {
class HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the work by P. Wawrzy´nski in
["A Cat-Like Robot Real-Time Learning to Run"](http://staff.elka.pw.edu.pl/~pwawrzyn/pub-s/0812_LSCLRR.pdf).
The HalfCheetah is a 2-dimensional robot consisting of 9 links and 8
joints connecting them (including two paws). The goal is to apply a torque
on the joints to make the cheetah run forward (right) as fast as possible,
with a positive reward allocated based on the distance moved forward and a
negative reward allocated for moving backward. The torso and head of the
cheetah are fixed, and the torque can only be applied on the other 6 joints
over the front and back thighs (connecting to the torso), shins
(connecting to the thighs) and feet (connecting to the shins).
### Action Space
The action space is a `Box(-1, 1, (6,), float32)`. An action represents the torques applied between *links*.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-------|--------------------------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the back thigh rotor | -1 | 1 | bthigh | hinge | torque (N m) |
| 1 | Torque applied on the back shin rotor | -1 | 1 | bshin | hinge | torque (N m) |
| 2 | Torque applied on the back foot rotor | -1 | 1 | bfoot | hinge | torque (N m) |
| 3 | Torque applied on the front thigh rotor| -1 | 1 | fthigh | hinge | torque (N m) |
| 4 | Torque applied on the front shin rotor | -1 | 1 | fshin | hinge | torque (N m) |
| 5 | Torque applied on the front foot rotor | -1 | 1 | ffoot | hinge | torque (N m) |
### Observation Space
Observations consist of positional values of different body parts of the
cheetah, followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
By default, observations do not include the x-coordinate of the cheetah's center of mass. It may
be included by passing `exclude_current_positions_from_observation=False` during construction.
In that case, the observation space will have 18 dimensions where the first dimension
represents the x-coordinate of the cheetah's center of mass.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate
will be returned in `info` with key `"x_position"`.
However, by default, the observation is a `ndarray` with shape `(17,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | z-coordinate of the front tip | -Inf | Inf | rootz | slide | position (m) |
| 1 | angle of the front tip | -Inf | Inf | rooty | hinge | angle (rad) |
| 2 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angle (rad) |
| 3 | angle of the second rotor | -Inf | Inf | bshin | hinge | angle (rad) |
| 4 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angle (rad) |
| 5 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge | angle (rad) |
| 6 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angle (rad) |
| 7 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angle (rad) |
| 8 | x-coordinate of the front tip | -Inf | Inf | rootx | slide | velocity (m/s) |
| 9 | y-coordinate of the front tip | -Inf | Inf | rootz | slide | velocity (m/s) |
| 10 | angle of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 11 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) |
| 12 | angle of the second rotor | -Inf | Inf | bshin | hinge | angular velocity (rad/s) |
| 13 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) |
| 14 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge |angular velocity (rad/s) |
| 15 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angular velocity (rad/s) |
| 16 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of two parts:
- *forward_reward*: A reward of moving forward which is measured
as *`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependent on the frame_skip parameter
(fixed to 5), where the frametime is 0.01 - making the
default *dt = 5 * 0.01 = 0.05*. This reward would be positive if the cheetah
runs forward (right).
- *ctrl_cost*: A cost for penalising the cheetah if it takes
actions that are too large. It is measured as *`ctrl_cost_weight` *
sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is a parameter set for the
control and has a default value of 0.1
The total reward returned is ***reward*** *=* *forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,) with a noise added to the
initial state for stochasticity. As seen before, the first 8 values in the
state are positional and the last 9 values are velocity. A uniform noise in
the range of [-`reset_noise_scale`, `reset_noise_scale`] is added to the positional values while a standard
normal noise with a mean of 0 and standard deviation of `reset_noise_scale` is added to the
initial velocity values of all zeros.
### Episode Termination
The episode terminates when the episode length is greater than 1000.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
env = gym.make('HalfCheetah-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('HalfCheetah-v3', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|----------------------|-------------------------------|
| `xml_file` | **str** | `"half_cheetah.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.0` | Weight for *forward_reward* term (see section on reward) |
| `ctrl_cost_weight` | **float** | `0.1` | Weight for *ctrl_cost* weight (see section on reward) |
| `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True` | Whether or not to omit the x-coordinate from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="half_cheetah.xml",
@@ -149,7 +30,7 @@ class HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5, mujoco_bindings="mujoco_py")
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))

230
gym/envs/mujoco/half_cheetah_v4.py Normal file
View File

@@ -0,0 +1,230 @@
__credits__ = ["Rushiv Arora"]
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
"distance": 4.0,
}
class HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the work by P. Wawrzy´nski in
["A Cat-Like Robot Real-Time Learning to Run"](http://staff.elka.pw.edu.pl/~pwawrzyn/pub-s/0812_LSCLRR.pdf).
The HalfCheetah is a 2-dimensional robot consisting of 9 links and 8
joints connecting them (including two paws). The goal is to apply a torque
on the joints to make the cheetah run forward (right) as fast as possible,
with a positive reward allocated based on the distance moved forward and a
negative reward allocated for moving backward. The torso and head of the
cheetah are fixed, and the torque can only be applied on the other 6 joints
over the front and back thighs (connecting to the torso), shins
(connecting to the thighs) and feet (connecting to the shins).
### Action Space
The agent take a 6-element vector for actions.
The action space is a continuous `(action, action, action, action, action, action)` all in `[-1.0, 1.0]`, where `action` represents the numerical torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-------|--------------------------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the back thigh rotor | -1 | 1 | bthigh | hinge | torque (N m) |
| 1 | Torque applied on the back shin rotor | -1 | 1 | bshin | hinge | torque (N m) |
| 2 | Torque applied on the back foot rotor | -1 | 1 | bfoot | hinge | torque (N m) |
| 3 | Torque applied on the front thigh rotor| -1 | 1 | fthigh | hinge | torque (N m) |
| 4 | Torque applied on the front shin rotor | -1 | 1 | fshin | hinge | torque (N m) |
| 5 | Torque applied on the front foot rotor | -1 | 1 | ffoot | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the
cheetah, followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(17,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | x-coordinate of the center of mass | -Inf | Inf | rootx | slide | position (m) |
| 1 | y-coordinate of the center of mass | -Inf | Inf | rootz | slide | position (m) |
| 2 | angle of the front tip | -Inf | Inf | rooty | hinge | angle (rad) |
| 3 | angle of the back thigh rotor | -Inf | Inf | bthigh | hinge | angle (rad) |
| 4 | angle of the back shin rotor | -Inf | Inf | bshin | hinge | angle (rad) |
| 5 | angle of the back foot rotor | -Inf | Inf | bfoot | hinge | angle (rad) |
| 6 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge | angle (rad) |
| 7 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angle (rad) |
| 8 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angle (rad) |
| 9 | x-coordinate of the front tip | -Inf | Inf | rootx | slide | velocity (m/s) |
| 10 | y-coordinate of the front tip | -Inf | Inf | rootz | slide | velocity (m/s) |
| 11 | angle of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 12 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) |
| 13 | angle of the second rotor | -Inf | Inf | bshin | hinge | angular velocity (rad/s) |
| 14 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) |
| 15 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge |angular velocity (rad/s) |
| 16 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angular velocity (rad/s) |
| 17 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angular velocity (rad/s) |
**Note:**
In practice (and Gym implementation), the first positional element is
omitted from the state space since the reward function is calculated based
on that value. This value is hidden from the algorithm, which in turn has
to develop an abstract understanding of it from the observed rewards.
Therefore, observation space has shape `(8,)` and looks like:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | y-coordinate of the front tip | -Inf | Inf | rootz | slide | position (m) |
| 1 | angle of the front tip | -Inf | Inf | rooty | hinge | angle (rad) |
| 2 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angle (rad) |
| 3 | angle of the second rotor | -Inf | Inf | bshin | hinge | angle (rad) |
| 4 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angle (rad) |
| 5 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge | angle (rad) |
| 6 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angle (rad) |
| 7 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angle (rad) |
| 8 | x-coordinate of the front tip | -Inf | Inf | rootx | slide | velocity (m/s) |
| 9 | y-coordinate of the front tip | -Inf | Inf | rootz | slide | velocity (m/s) |
| 10 | angle of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 11 | angle of the second rotor | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) |
| 12 | angle of the second rotor | -Inf | Inf | bshin | hinge | angular velocity (rad/s) |
| 13 | velocity of the tip along the x-axis | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) |
| 14 | velocity of the tip along the y-axis | -Inf | Inf | fthigh | hinge |angular velocity (rad/s) |
| 15 | angular velocity of front tip | -Inf | Inf | fshin | hinge | angular velocity (rad/s) |
| 16 | angular velocity of second rotor | -Inf | Inf | ffoot | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of two parts:
- *reward_run*: A reward of moving forward which is measured
as *(x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependeent on the frame_skip parameter
(default is 5), where the *dt* for one frame is 0.01 - making the
default *dt = 5*0.01 = 0.05*. This reward would be positive if the cheetah
runs forward (right) desired.
- *reward_control*: A negative reward for penalising the cheetah if it takes
actions that are too large. It is measured as *-coefficient x
sum(action<sup>2</sup>)* where *coefficient* is a parameter set for the
control and has a default value of 0.1
The total reward returned is ***reward*** *=* *reward_run + reward_control*
### Starting State
All observations start in state (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,) with a noise added to the
initial state for stochasticity. As seen before, the first 8 values in the
state are positional and the last 9 values are velocity. A uniform noise in
the range of [-0.1, 0.1] is added to the positional values while a standard
normal noise with a mean of 0 and standard deviation of 0.1 is added to the
initial velocity values of all zeros.
### Episode Termination
The episode terminates when the episode length is greater than 1000.
### Arguments
No additional arguments are currently supported (in v2 and lower), but
modifications can be made to the XML file in the assets folder at
`gym/envs/mujoco/assets/half_cheetah.xml` (or by changing the path to a
modified XML file in another folder).
```
env = gym.make('HalfCheetah-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('HalfCheetah-v3', ctrl_cost_weight=0.1, ....)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="half_cheetah.xml",
forward_reward_weight=1.0,
ctrl_cost_weight=0.1,
reset_noise_scale=0.1,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
def step(self, action):
x_position_before = self.data.qpos[0]
self.do_simulation(action, self.frame_skip)
x_position_after = self.data.qpos[0]
x_velocity = (x_position_after - x_position_before) / self.dt
ctrl_cost = self.control_cost(action)
forward_reward = self._forward_reward_weight * x_velocity
observation = self._get_obs()
reward = forward_reward - ctrl_cost
done = False
info = {
"x_position": x_position_after,
"x_velocity": x_velocity,
"reward_run": forward_reward,
"reward_ctrl": -ctrl_cost,
}
return observation, reward, done, info
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = self.data.qvel.flat.copy()
if self._exclude_current_positions_from_observation:
position = position[1:]
observation = np.concatenate((position, velocity)).ravel()
return observation
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = (
self.init_qvel
+ self._reset_noise_scale * self.np_random.standard_normal(self.model.nv)
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

4
gym/envs/mujoco/hopper.py
View File

@@ -6,7 +6,9 @@ from gym.envs.mujoco import mujoco_env
class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "hopper.xml", 4)
mujoco_env.MujocoEnv.__init__(
self, "hopper.xml", 4, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def step(self, a):

126
gym/envs/mujoco/hopper_v3.py
View File

@@ -14,130 +14,6 @@ DEFAULT_CAMERA_CONFIG = {
class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the work done by Erez, Tassa, and Todorov in
["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf). The environment aims to
increase the number of independent state and control variables as compared to
the classic control environments. The hopper is a two-dimensional
one-legged figure that consist of four main body parts - the torso at the
top, the thigh in the middle, the leg in the bottom, and a single foot on
which the entire body rests. The goal is to make hops that move in the
forward (right) direction by applying torques on the three hinges
connecting the four body parts.
### Action Space
The action space is a `Box(-1, 1, (3,), float32)`. An action represents the torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
| 3 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
### Observation Space
Observations consist of positional values of different body parts of the
hopper, followed by the velocities of those individual parts
(their derivatives) with all the positions ordered before all the velocities.
By default, observations do not include the x-coordinate of the hopper. It may
be included by passing `exclude_current_positions_from_observation=False` during construction.
In that case, the observation space will have 12 dimensions where the first dimension
represents the x-coordinate of the hopper.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate
will be returned in `info` with key `"x_position"`.
However, by default, the observation is a `ndarray` with shape `(11,)` where the elements
correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
| 1 | angle of the top | -Inf | Inf | rooty | hinge | angle (rad) |
| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 5 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 6 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 7 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 8 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 9 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 10 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of three parts:
- *healthy_reward*: Every timestep that the hopper is healthy (see definition in section "Episode Termination"), it gets a reward of fixed value `healthy_reward`.
- *forward_reward*: A reward of hopping forward which is measured
as *`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependent on the frame_skip parameter
(fixed to 4), where the frametime is 0.002 - making the
default *dt = 4 * 0.002 = 0.008*. This reward would be positive if the hopper
hops forward (positive x direction).
- *ctrl_cost*: A cost for penalising the hopper if it takes
actions that are too large. It is measured as *`ctrl_cost_weight` *
sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is a parameter set for the
control and has a default value of 0.001
The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state
(0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise
in the range of [-`reset_noise_scale`, `reset_noise_scale`] added to the values for stochasticity.
### Episode Termination
The hopper is said to be unhealthy if any of the following happens:
1. An element of `observation[1:]` (if `exclude_current_positions_from_observation=True`, else `observation[2:]`) is no longer contained in the closed interval specified by the argument `healthy_state_range`
2. The height of the hopper (`observation[0]` if `exclude_current_positions_from_observation=True`, else `observation[1]`) is no longer contained in the closed interval specified by the argument `healthy_z_range` (usually meaning that it has fallen)
3. The angle (`observation[1]` if `exclude_current_positions_from_observation=True`, else `observation[2]`) is no longer contained in the closed interval specified by the argument `healthy_angle_range`
If `terminate_when_unhealthy=True` is passed during construction (which is the default),
the episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. The hopper is unhealthy
If `terminate_when_unhealthy=False` is passed, the episode is terminated only when 1000 timesteps are exceeded.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
env = gym.make('Hopper-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Hopper-v3', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|----------------|-------------------------------|
| `xml_file` | **str** | `"hopper.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.0` | Weight for *forward_reward* term (see section on reward) |
| `ctrl_cost_weight` | **float** | `0.001` | Weight for *ctrl_cost* reward (see section on reward) |
| `healthy_reward` | **float** | `1` | Constant reward given if the ant is "healthy" after timestep |
| `terminate_when_unhealthy` | **bool**| `True` | If true, issue a done signal if the hopper is no longer healthy |
| `healthy_state_range` | **tuple** | `(-100, 100)` | The elements of `observation[1:]` (if `exclude_current_positions_from_observation=True`, else `observation[2:]`) must be in this range for the hopper to be considered healthy |
| `healthy_z_range` | **tuple** | `(0.7, float("inf"))` | The z-coordinate must be in this range for the hopper to be considered healthy |
| `healthy_angle_range` | **tuple** | `(-0.2, 0.2)` | The angle given by `observation[1]` (if `exclude_current_positions_from_observation=True`, else `observation[2]`) must be in this range for the hopper to be considered healthy |
| `reset_noise_scale` | **float** | `5e-3` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True`| Whether or not to omit the x-coordinate from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="hopper.xml",
@@ -170,7 +46,7 @@ class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4, mujoco_bindings="mujoco_py")
@property
def healthy_reward(self):

263
gym/envs/mujoco/hopper_v4.py Normal file
View File

@@ -0,0 +1,263 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 2,
"distance": 3.0,
"lookat": np.array((0.0, 0.0, 1.15)),
"elevation": -20.0,
}
class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the work done by Erez, Tassa, and Todorov in
["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf). The environment aims to
increase the number of independent state and control variables as compared to
the classic control environments. The hopper is a two-dimensional
one-legged figure that consist of four main body parts - the torso at the
top, the thigh in the middle, the leg in the bottom, and a single foot on
which the entire body rests. The goal is to make hops that move in the
forward (right) direction by applying torques on the three hinges
connecting the four body parts.
### Action Space
The agent take a 3-element vector for actions.
The action space is a continuous `(action, action, action)` all in `[-1, 1]`
, where `action` represents the numerical torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
| 3 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the
hopper, followed by the velocities of those individual parts
(their derivatives) with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(11,)` where the elements
correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | x-coordinate of the top | -Inf | Inf | rootx | slide | position (m) |
| 1 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
| 2 | angle of the top | -Inf | Inf | rooty | hinge | angle (rad) |
| 3 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 4 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 5 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 6 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 7 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 8 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 9 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 10 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 11 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
**Note:**
In practice (and Gym implementation), the first positional element is
omitted from the state space since the reward function is calculated based
on that value. This value is hidden from the algorithm, which in turn has
to develop an abstract understanding of it from the observed rewards.
Therefore, observation space has shape `(11,)` instead of `(12,)` and looks like:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz | slide | position (m) |
| 1 | angle of the top | -Inf | Inf | rooty | hinge | angle (rad) |
| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 5 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 6 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 7 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 8 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 9 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 10 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of three parts:
- *alive bonus*: Every timestep that the hopper is alive, it gets a reward of 1,
- *reward_forward*: A reward of hopping forward which is measured
as *(x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependeent on the frame_skip parameter
(default is 4), where the *dt* for one frame is 0.002 - making the
default *dt = 4*0.002 = 0.008*. This reward would be positive if the hopper
hops forward (right) desired.
- *reward_control*: A negative reward for penalising the hopper if it takes
actions that are too large. It is measured as *-coefficient **x**
sum(action<sup>2</sup>)* where *coefficient* is a parameter set for the
control and has a default value of 0.001
The total reward returned is ***reward*** *=* *alive bonus + reward_forward + reward_control*
### Starting State
All observations start in state
(0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise
in the range of [-0.005, 0.005] added to the values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
3. The absolute value of any of the state variable indexed (angle and beyond) is greater than 100
4. The height of the hopper becomes greater than 0.7 metres (hopper has hopped too high).
5. The absolute value of the angle (index 2) is less than 0.2 radians (hopper has fallen down).
### Arguments
No additional arguments are currently supported (in v2 and lower), but
modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder).
```
env = gym.make('Hopper-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Hopper-v3', ctrl_cost_weight=0.1, ....)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="hopper.xml",
forward_reward_weight=1.0,
ctrl_cost_weight=1e-3,
healthy_reward=1.0,
terminate_when_unhealthy=True,
healthy_state_range=(-100.0, 100.0),
healthy_z_range=(0.7, float("inf")),
healthy_angle_range=(-0.2, 0.2),
reset_noise_scale=5e-3,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_state_range = healthy_state_range
self._healthy_z_range = healthy_z_range
self._healthy_angle_range = healthy_angle_range
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
@property
def healthy_reward(self):
return (
float(self.is_healthy or self._terminate_when_unhealthy)
* self._healthy_reward
)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
@property
def is_healthy(self):
z, angle = self.data.qpos[1:3]
state = self.state_vector()[2:]
min_state, max_state = self._healthy_state_range
min_z, max_z = self._healthy_z_range
min_angle, max_angle = self._healthy_angle_range
healthy_state = np.all(np.logical_and(min_state < state, state < max_state))
healthy_z = min_z < z < max_z
healthy_angle = min_angle < angle < max_angle
is_healthy = all((healthy_state, healthy_z, healthy_angle))
return is_healthy
@property
def done(self):
done = not self.is_healthy if self._terminate_when_unhealthy else False
return done
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = np.clip(self.data.qvel.flat.copy(), -10, 10)
if self._exclude_current_positions_from_observation:
position = position[1:]
observation = np.concatenate((position, velocity)).ravel()
return observation
def step(self, action):
x_position_before = self.data.qpos[0]
self.do_simulation(action, self.frame_skip)
x_position_after = self.data.qpos[0]
x_velocity = (x_position_after - x_position_before) / self.dt
ctrl_cost = self.control_cost(action)
forward_reward = self._forward_reward_weight * x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
costs = ctrl_cost
observation = self._get_obs()
reward = rewards - costs
done = self.done
info = {
"x_position": x_position_after,
"x_velocity": x_velocity,
}
return observation, reward, done, info
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nv
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

4
gym/envs/mujoco/humanoid.py
View File

@@ -12,7 +12,9 @@ def mass_center(model, sim):
class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "humanoid.xml", 5)
mujoco_env.MujocoEnv.__init__(
self, "humanoid.xml", 5, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def _get_obs(self):

194
gym/envs/mujoco/humanoid_v3.py
View File

@@ -18,198 +18,6 @@ def mass_center(model, sim):
class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Tassa, Erez and Todorov
in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a pair of
legs and arms. The legs each consist of two links, and so the arms (representing the knees and
elbows respectively). The goal of the environment is to walk forward as fast as possible without falling over.
### Action Space
The action space is a `Box(-1, 1, (17,), float32)`. An action represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between torso/abdomen and the right hip (x-coordinate) | -0.4 | 0.4 | right_hip_x (right_thigh) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between torso/abdomen and the right hip (z-coordinate) | -0.4 | 0.4 | right_hip_z (right_thigh) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between torso/abdomen and the right hip (y-coordinate) | -0.4 | 0.4 | right_hip_y (right_thigh) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the right hip/thigh and the right shin | -0.4 | 0.4 | right_knee | hinge | torque (N m) |
| 7 | Torque applied on the rotor between torso/abdomen and the left hip (x-coordinate) | -0.4 | 0.4 | left_hip_x (left_thigh) | hinge | torque (N m) |
| 8 | Torque applied on the rotor between torso/abdomen and the left hip (z-coordinate) | -0.4 | 0.4 | left_hip_z (left_thigh) | hinge | torque (N m) |
| 9 | Torque applied on the rotor between torso/abdomen and the left hip (y-coordinate) | -0.4 | 0.4 | left_hip_y (left_thigh) | hinge | torque (N m) |
| 10 | Torque applied on the rotor between the left hip/thigh and the left shin | -0.4 | 0.4 | left_knee | hinge | torque (N m) |
| 11 | Torque applied on the rotor between the torso and right upper arm (coordinate -1) | -0.4 | 0.4 | right_shoulder1 | hinge | torque (N m) |
| 12 | Torque applied on the rotor between the torso and right upper arm (coordinate -2) | -0.4 | 0.4 | right_shoulder2 | hinge | torque (N m) |
| 13 | Torque applied on the rotor between the right upper arm and right lower arm | -0.4 | 0.4 | right_elbow | hinge | torque (N m) |
| 14 | Torque applied on the rotor between the torso and left upper arm (coordinate -1) | -0.4 | 0.4 | left_shoulder1 | hinge | torque (N m) |
| 15 | Torque applied on the rotor between the torso and left upper arm (coordinate -2) | -0.4 | 0.4 | left_shoulder2 | hinge | torque (N m) |
| 16 | Torque applied on the rotor between the left upper arm and left lower arm | -0.4 | 0.4 | left_elbow | hinge | torque (N m) |
### Observation Space
Observations consist of positional values of different body parts of the Humanoid,
followed by the velocities of those individual parts (their derivatives) with all the
positions ordered before all the velocities.
By default, observations do not include the x- and y-coordinates of the torso. These may
be included by passing `exclude_current_positions_from_observation=False` during construction.
In that case, the observation space will have 378 dimensions where the first two dimensions
represent the x- and y-coordinates of the torso.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
However, by default, the observation is a `ndarray` with shape `(376,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 1 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 2 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 3 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 4 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 5 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
| 6 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
| 7 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
| 8 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
| 9 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
| 19 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
| 11 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
| 12 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
| 13 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
| 14 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
| 15 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
| 16 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
| 17 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
| 18 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
| 19 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
| 20 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
| 21 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
| 22 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 23 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 24 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 25 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 26 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 27 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 28 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | anglular velocity (rad/s) |
| 29 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | anglular velocity (rad/s) |
| 30 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | aanglular velocity (rad/s) |
| 31 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | anglular velocity (rad/s) |
| 32 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | anglular velocity (rad/s) |
| 33 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | anglular velocity (rad/s) |
| 34 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | anglular velocity (rad/s) |
| 35 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | anglular velocity (rad/s) |
| 36 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | anglular velocity (rad/s) |
| 37 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | anglular velocity (rad/s) |
| 38 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | anglular velocity (rad/s) |
| 39 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | anglular velocity (rad/s) |
| 40 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | anglular velocity (rad/s) |
| 41 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | anglular velocity (rad/s) |
| 42 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | anglular velocity (rad/s) |
| 43 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | anglular velocity (rad/s) |
| 44 | angular velocitty of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | anglular velocity (rad/s) |
Additionally, after all the positional and velocity based values in the table,
the observation contains (in order):
- *cinert:* Mass and inertia of a single rigid body relative to the center of mass
(this is an intermediate result of transition). It has shape 14*10 (*nbody * 10*)
and hence adds to another 140 elements in the state space.
- *cvel:* Center of mass based velocity. It has shape 14 * 6 (*nbody * 6*) and hence
adds another 84 elements in the state space
- *qfrc_actuator:* Constraint force generated as the actuator force. This has shape
`(23,)` *(nv * 1)* and hence adds another 23 elements to the state space.
- *cfrc_ext:* This is the center of mass based external force on the body. It has shape
14 * 6 (*nbody * 6*) and hence adds to another 84 elements in the state space.
where *nbody* stands for the number of bodies in the robot and *nv* stands for the
number of degrees of freedom (*= dim(qvel)*)
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the
[Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There have been reported issues that using a Mujoco-Py version > 2.0
results in the contact forces always being 0. As such we recommend to use a Mujoco-Py
version < 2.0 when using the Humanoid environment if you would like to report results
with contact forces (if contact forces are not used in your experiments, you can use
version > 2.0).
### Rewards
The reward consists of three parts:
- *healthy_reward*: Every timestep that the humanoid is alive (see section Episode Termination for definition), it gets a reward of fixed value `healthy_reward`
- *forward_reward*: A reward of walking forward which is measured as *`forward_reward_weight` *
(average center of mass before action - average center of mass after action)/dt*.
*dt* is the time between actions and is dependent on the frame_skip parameter
(default is 5), where the frametime is 0.003 - making the default *dt = 5 * 0.003 = 0.015*.
This reward would be positive if the humanoid walks forward (in positive x-direction). The calculation
for the center of mass is defined in the `.py` file for the Humanoid.
- *ctrl_cost*: A negative reward for penalising the humanoid if it has too
large of a control force. If there are *nu* actuators/controls, then the control has
shape `nu x 1`. It is measured as *`ctrl_cost_weight` * sum(control<sup>2</sup>)*.
- *contact_cost*: A negative reward for penalising the humanoid if the external
contact force is too large. It is calculated by clipping
*`contact_cost_weight` * sum(external contact force<sup>2</sup>)* to the interval specified by `contact_cost_range`.
The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state
(0.0, 0.0, 1.4, 1.0, 0.0 ... 0.0) with a uniform noise in the range
of [-`reset_noise_scale`, `reset_noise_scale`] added to the positional and velocity values (values in the table)
for stochasticity. Note that the initial z coordinate is intentionally
selected to be high, thereby indicating a standing up humanoid. The initial
orientation is designed to make it face forward as well.
### Episode Termination
The humanoid is said to be unhealthy if the z-position of the torso is no longer contained in the
closed interval specified by the argument `healthy_z_range`.
If `terminate_when_unhealthy=True` is passed during construction (which is the default),
the episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
3. The humanoid is unhealthy
If `terminate_when_unhealthy=False` is passed, the episode is terminated only when 1000 timesteps are exceeded.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
env = gym.make('Humanoid-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Humanoid-v3', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|-------------------|-------------------------------|
| `xml_file` | **str** | `"humanoid.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.25` | Weight for *forward_reward* term (see section on reward) |
| `ctrl_cost_weight` | **float** | `0.1` | Weight for *ctrl_cost* term (see section on reward) |
| `contact_cost_weight` | **float** | `5e-7` | Weight for *contact_cost* term (see section on reward) |
| `healthy_reward` | **float** | `5.0` | Constant reward given if the humanoid is "healthy" after timestep |
| `terminate_when_unhealthy` | **bool**| `True` | If true, issue a done signal if the z-coordinate of the torso is no longer in the `healthy_z_range` |
| `healthy_z_range` | **tuple** | `(1.0, 2.0)` | The humanoid is considered healthy if the z-coordinate of the torso is in this range |
| `reset_noise_scale` | **float** | `1e-2` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True`| Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="humanoid.xml",
@@ -239,7 +47,7 @@ class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5, mujoco_bindings="mujoco_py")
@property
def healthy_reward(self):

331
gym/envs/mujoco/humanoid_v4.py Normal file
View File

@@ -0,0 +1,331 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 1,
"distance": 4.0,
"lookat": np.array((0.0, 0.0, 2.0)),
"elevation": -20.0,
}
def mass_center(model, data):
mass = np.expand_dims(model.body_mass, axis=1)
xpos = data.xipos
return (np.sum(mass * xpos, axis=0) / np.sum(mass))[0:2].copy()
class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Tassa, Erez and Todorov
in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a pair of
legs and arms. The legs each consist of two links, and so the arms (representing the knees and
elbows respectively). The goal of the environment is to walk forward as fast as possible without falling over.
### Action Space
The agent take a 17-element vector for actions.
The action space is a continuous `(action, ...)` all in `[-1, 1]`, where `action`
represents the numerical torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between torso/abdomen and the right hip (x-coordinate) | -0.4 | 0.4 | right_hip_x (right_thigh) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between torso/abdomen and the right hip (z-coordinate) | -0.4 | 0.4 | right_hip_z (right_thigh) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between torso/abdomen and the right hip (y-coordinate) | -0.4 | 0.4 | right_hip_y (right_thigh) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the right hip/thigh and the right shin | -0.4 | 0.4 | right_knee | hinge | torque (N m) |
| 7 | Torque applied on the rotor between torso/abdomen and the left hip (x-coordinate) | -0.4 | 0.4 | left_hip_x (left_thigh) | hinge | torque (N m) |
| 8 | Torque applied on the rotor between torso/abdomen and the left hip (z-coordinate) | -0.4 | 0.4 | left_hip_z (left_thigh) | hinge | torque (N m) |
| 9 | Torque applied on the rotor between torso/abdomen and the left hip (y-coordinate) | -0.4 | 0.4 | left_hip_y (left_thigh) | hinge | torque (N m) |
| 10 | Torque applied on the rotor between the left hip/thigh and the left shin | -0.4 | 0.4 | left_knee | hinge | torque (N m) |
| 11 | Torque applied on the rotor between the torso and right upper arm (coordinate -1) | -0.4 | 0.4 | right_shoulder1 | hinge | torque (N m) |
| 12 | Torque applied on the rotor between the torso and right upper arm (coordinate -2) | -0.4 | 0.4 | right_shoulder2 | hinge | torque (N m) |
| 13 | Torque applied on the rotor between the right upper arm and right lower arm | -0.4 | 0.4 | right_elbow | hinge | torque (N m) |
| 14 | Torque applied on the rotor between the torso and left upper arm (coordinate -1) | -0.4 | 0.4 | left_shoulder1 | hinge | torque (N m) |
| 15 | Torque applied on the rotor between the torso and left upper arm (coordinate -2) | -0.4 | 0.4 | left_shoulder2 | hinge | torque (N m) |
| 16 | Torque applied on the rotor between the left upper arm and left lower arm | -0.4 | 0.4 | left_elbow | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the Humanoid,
followed by the velocities of those individual parts (their derivatives) with all the
positions ordered before all the velocities.
The observation is a `ndarray` with shape `(376,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 1 | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 2 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 3 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 4 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 5 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 6 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 7 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
| 8 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
| 9 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
| 10 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
| 11 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
| 12 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
| 13 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
| 14 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
| 15 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
| 16 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
| 17 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
| 18 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
| 19 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
| 20 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
| 21 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
| 22 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
| 23 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
| 24 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 25 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 26 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 27 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 28 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 29 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 30 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | anglular velocity (rad/s) |
| 31 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | anglular velocity (rad/s) |
| 32 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | aanglular velocity (rad/s) |
| 33 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | anglular velocity (rad/s) |
| 34 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | anglular velocity (rad/s) |
| 35 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | anglular velocity (rad/s) |
| 36 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | anglular velocity (rad/s) |
| 37 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | anglular velocity (rad/s) |
| 38 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | anglular velocity (rad/s) |
| 39 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | anglular velocity (rad/s) |
| 40 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | anglular velocity (rad/s) |
| 41 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | anglular velocity (rad/s) |
| 42 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | anglular velocity (rad/s) |
| 43 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | anglular velocity (rad/s) |
| 44 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | anglular velocity (rad/s) |
| 45 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | anglular velocity (rad/s) |
| 46 | angular velocitty of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | anglular velocity (rad/s) |
Additionally, after all the positional and velocity based values in the table,
the state_space consists of (in order):
- *cinert:* Mass and inertia of a single rigid body relative to the center of mass
(this is an intermediate result of transition). It has shape 14*10 (*nbody * 10*)
and hence adds to another 140 elements in the state space.
- *cvel:* Center of mass based velocity. It has shape 14 * 6 (*nbody * 6*) and hence
adds another 84 elements in the state space
- *qfrc_actuator:* Constraint force generated as the actuator force. This has shape
`(23,)` *(nv * 1)* and hence adds another 23 elements to the state space.
- *cfrc_ext:* This is the center of mass based external force on the body. It has shape
14 * 6 (*nbody * 6*) and hence adds to another 84 elements in the state space.
where *nbody* stands for the number of bodies in the robot and *nv* stands for the
number of degrees of freedom (*= dim(qvel)*)
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the
[Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There are 47 elements in the table above - giving rise to `(378,)`
elements in the state space. In practice (and Gym implementation), the first two
positional elements are omitted from the state space since the reward function is
calculated based on the x-coordinate value. This value is hidden from the algorithm,
which in turn has to develop an abstract understanding of it from the observed rewards.
Therefore, observation space has shape `(376,)` instead of `(378,)` and the table should
not have the first two rows.
**Note:** Humanoid-v4 environment no longer has the following contact forces issue.
If using previous Humanoid versions from v4, there have been reported issues that using
a Mujoco-Py version > 2.0 results in the contact forces always being 0. As such we recommend
to use a Mujoco-Py version < 2.0 when using the Humanoid environment if you would like to report
results with contact forces (if contact forces are not used in your experiments, you can use
version > 2.0).
### Rewards
The reward consists of three parts:
- *alive_bonus*: Every timestep that the humanoid is alive, it gets a reward of 5.
- *lin_vel_cost*: A reward of walking forward which is measured as *1.25 *
(average center of mass before action - average center of mass after action)/dt*.
*dt* is the time between actions and is dependent on the frame_skip parameter
(default is 5), where the *dt* for one frame is 0.003 - making the default *dt = 5 * 0.003 = 0.015*.
This reward would be positive if the humanoid walks forward (right) desired. The calculation
for the center of mass is defined in the `.py` file for the Humanoid.
- *quad_ctrl_cost*: A negative reward for penalising the humanoid if it has too
large of a control force. If there are *nu* actuators/controls, then the control has
shape `nu x 1`. It is measured as *0.1 **x** sum(control<sup>2</sup>)*.
- *quad_impact_cost*: A negative reward for penalising the humanoid if the external
contact force is too large. It is calculated as
*min(0.5 * 0.000001 * sum(external contact force<sup>2</sup>), 10)*.
The total reward returned is ***reward*** *=* *alive_bonus + lin_vel_cost - quad_ctrl_cost - quad_impact_cost*
### Starting State
All observations start in state
(0.0, 0.0, 1.4, 1.0, 0.0 ... 0.0) with a uniform noise in the range
of [-0.01, 0.01] added to the positional and velocity values (values in the table)
for stochasticity. Note that the initial z coordinate is intentionally
selected to be high, thereby indicating a standing up humanoid. The initial
orientation is designed to make it face forward as well.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
3. The z-coordinate of the torso (index 0 in state space OR index 2 in the table) is **not** in the range `[1.0, 2.0]` (the humanoid has fallen or is about to fall beyond recovery).
### Arguments
No additional arguments are currently supported (in v2 and lower), but
modifications can be made to the XML file in the assets folder (or by changing
the path to a modified XML file in another folder)..
```
env = gym.make('Ant-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Ant-v3', ctrl_cost_weight=0.1, ....)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="humanoid.xml",
forward_reward_weight=1.25,
ctrl_cost_weight=0.1,
healthy_reward=5.0,
terminate_when_unhealthy=True,
healthy_z_range=(1.0, 2.0),
reset_noise_scale=1e-2,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_z_range = healthy_z_range
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 5)
@property
def healthy_reward(self):
return (
float(self.is_healthy or self._terminate_when_unhealthy)
* self._healthy_reward
)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(self.data.ctrl))
return control_cost
@property
def is_healthy(self):
min_z, max_z = self._healthy_z_range
is_healthy = min_z < self.data.qpos[2] < max_z
return is_healthy
@property
def done(self):
done = (not self.is_healthy) if self._terminate_when_unhealthy else False
return done
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = self.data.qvel.flat.copy()
com_inertia = self.data.cinert.flat.copy()
com_velocity = self.data.cvel.flat.copy()
actuator_forces = self.data.qfrc_actuator.flat.copy()
external_contact_forces = self.data.cfrc_ext.flat.copy()
if self._exclude_current_positions_from_observation:
position = position[2:]
return np.concatenate(
(
position,
velocity,
com_inertia,
com_velocity,
actuator_forces,
external_contact_forces,
)
)
def step(self, action):
xy_position_before = mass_center(self.model, self.data)
self.do_simulation(action, self.frame_skip)
xy_position_after = mass_center(self.model, self.data)
xy_velocity = (xy_position_after - xy_position_before) / self.dt
x_velocity, y_velocity = xy_velocity
ctrl_cost = self.control_cost(action)
forward_reward = self._forward_reward_weight * x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
observation = self._get_obs()
reward = rewards - ctrl_cost
done = self.done
info = {
"reward_linvel": forward_reward,
"reward_quadctrl": -ctrl_cost,
"reward_alive": healthy_reward,
"x_position": xy_position_after[0],
"y_position": xy_position_after[1],
"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
"x_velocity": x_velocity,
"y_velocity": y_velocity,
"forward_reward": forward_reward,
}
return observation, reward, done, info
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nv
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

174
gym/envs/mujoco/humanoidstandup.py
View File

@@ -5,178 +5,10 @@ from gym.envs.mujoco import mujoco_env
class HumanoidStandupEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Tassa, Erez and Todorov
in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a
pair of legs and arms. The legs each consist of two links, and so the arms (representing the
knees and elbows respectively). The environment starts with the humanoid layiing on the ground,
and then the goal of the environment is to make the humanoid standup and then keep it standing
by applying torques on the various hinges.
### Action Space
The agent take a 17-element vector for actions.
The action space is a continuous `(action, ...)` all in `[-1, 1]`, where `action`
represents the numerical torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between torso/abdomen and the right hip (x-coordinate) | -0.4 | 0.4 | right_hip_x (right_thigh) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between torso/abdomen and the right hip (z-coordinate) | -0.4 | 0.4 | right_hip_z (right_thigh) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between torso/abdomen and the right hip (y-coordinate) | -0.4 | 0.4 | right_hip_y (right_thigh) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the right hip/thigh and the right shin | -0.4 | 0.4 | right_knee | hinge | torque (N m) |
| 7 | Torque applied on the rotor between torso/abdomen and the left hip (x-coordinate) | -0.4 | 0.4 | left_hip_x (left_thigh) | hinge | torque (N m) |
| 8 | Torque applied on the rotor between torso/abdomen and the left hip (z-coordinate) | -0.4 | 0.4 | left_hip_z (left_thigh) | hinge | torque (N m) |
| 9 | Torque applied on the rotor between torso/abdomen and the left hip (y-coordinate) | -0.4 | 0.4 | left_hip_y (left_thigh) | hinge | torque (N m) |
| 10 | Torque applied on the rotor between the left hip/thigh and the left shin | -0.4 | 0.4 | left_knee | hinge | torque (N m) |
| 11 | Torque applied on the rotor between the torso and right upper arm (coordinate -1) | -0.4 | 0.4 | right_shoulder1 | hinge | torque (N m) |
| 12 | Torque applied on the rotor between the torso and right upper arm (coordinate -2) | -0.4 | 0.4 | right_shoulder2 | hinge | torque (N m) |
| 13 | Torque applied on the rotor between the right upper arm and right lower arm | -0.4 | 0.4 | right_elbow | hinge | torque (N m) |
| 14 | Torque applied on the rotor between the torso and left upper arm (coordinate -1) | -0.4 | 0.4 | left_shoulder1 | hinge | torque (N m) |
| 15 | Torque applied on the rotor between the torso and left upper arm (coordinate -2) | -0.4 | 0.4 | left_shoulder2 | hinge | torque (N m) |
| 16 | Torque applied on the rotor between the left upper arm and left lower arm | -0.4 | 0.4 | left_elbow | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the Humanoid,
followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
**Note:** The x- and y-coordinates of the torso are being omitted to produce position-agnostic behavior in policies
The observation is a `ndarray` with shape `(376,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 1 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 2 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 3 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 4 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 5 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
| 6 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
| 7 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
| 8 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
| 9 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
| 10 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
| 11 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
| 12 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
| 13 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
| 14 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
| 15 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
| 16 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
| 17 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
| 18 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
| 19 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
| 20 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
| 21 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
| 22 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 23 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 24 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 25 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 26 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 27 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 28 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | anglular velocity (rad/s) |
| 29 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | anglular velocity (rad/s) |
| 30 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | aanglular velocity (rad/s) |
| 31 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | anglular velocity (rad/s) |
| 32 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | anglular velocity (rad/s) |
| 33 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | anglular velocity (rad/s) |
| 35 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | anglular velocity (rad/s) |
| 36 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | anglular velocity (rad/s) |
| 37 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | anglular velocity (rad/s) |
| 38 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | anglular velocity (rad/s) |
| 39 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | anglular velocity (rad/s) |
| 40 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | anglular velocity (rad/s) |
| 41 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | anglular velocity (rad/s) |
| 42 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | anglular velocity (rad/s) |
| 43 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | anglular velocity (rad/s) |
| 44 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | anglular velocity (rad/s) |
| 45 | angular velocitty of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | anglular velocity (rad/s) |
Additionally, after all the positional and velocity based values in the table,
the state_space consists of (in order):
- *cinert:* Mass and inertia of a single rigid body relative to the center of mass
(this is an intermediate result of transition). It has shape 14*10 (*nbody * 10*)
and hence adds to another 140 elements in the state space.
- *cvel:* Center of mass based velocity. It has shape 14 * 6 (*nbody * 6*) and hence
adds another 84 elements in the state space
- *qfrc_actuator:* Constraint force generated as the actuator force. This has shape
`(23,)` *(nv * 1)* and hence adds another 23 elements to the state space.
- *cfrc_ext:* This is the center of mass based external force on the body. It has shape
14 * 6 (*nbody * 6*) and hence adds to another 84 elements in the state space.
where *nbody* stands for the number of bodies in the robot and *nv* stands for the number
of degrees of freedom (*= dim(qvel)*)
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the
[Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There have been reported issues that using a Mujoco-Py version > 2.0 results
in the contact forces always being 0. As such we recommend to use a Mujoco-Py version < 2.0
when using the Humanoid environment if you would like to report results with contact forces
(if contact forces are not used in your experiments, you can use version > 2.0).
### Rewards
The reward consists of three parts:
- *uph_cost*: A reward for moving upward (in an attempt to stand up). This is not a relative
reward which meaures how much upward it has moved from the last timestep, but it is an
absolute reward which measures how much upward the Humanoid has moved overall. It is
measured as *(z coordinate after action - 0)/(atomic timestep)*, where *z coordinate after
action* is index 0 in the state/index 2 in the table, and *atomic timestep* is the time for
one frame of movement even though the simulation has a framerate of 5 (done in order to inflate
rewards a little for fassteer learning).
- *quad_ctrl_cost*: A negative reward for penalising the humanoid if it has too large of
a control force. If there are *nu* actuators/controls, then the control has shape `nu x 1`.
It is measured as *0.1 **x** sum(control<sup>2</sup>)*.
- *quad_impact_cost*: A negative reward for penalising the humanoid if the external
contact force is too large. It is calculated as *min(0.5 * 0.000001 * sum(external
contact force<sup>2</sup>), 10)*.
The total reward returned is ***reward*** *=* *uph_cost + 1 - quad_ctrl_cost - quad_impact_cost*
### Starting State
All observations start in state
(0.0, 0.0, 0.105, 1.0, 0.0 ... 0.0) with a uniform noise in the range of
[-0.01, 0.01] added to the positional and velocity values (values in the table)
for stochasticity. Note that the initial z coordinate is intentionally selected
to be low, thereby indicating a laying down humanoid. The initial orientation is
designed to make it face forward as well.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
### Arguments
No additional arguments are currently supported.
```
env = gym.make('HumanoidStandup-v2')
```
There is no v3 for HumanoidStandup, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "humanoidstandup.xml", 5)
mujoco_env.MujocoEnv.__init__(
self, "humanoidstandup.xml", 5, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def _get_obs(self):

247
gym/envs/mujoco/humanoidstandup_v4.py Normal file
View File

@@ -0,0 +1,247 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class HumanoidStandupEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is based on the environment introduced by Tassa, Erez and Todorov
in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a
pair of legs and arms. The legs each consist of two links, and so the arms (representing the
knees and elbows respectively). The environment starts with the humanoid layiing on the ground,
and then the goal of the environment is to make the humanoid standup and then keep it standing
by applying torques on the various hinges.
### Action Space
The agent take a 17-element vector for actions.
The action space is a continuous `(action, ...)` all in `[-1, 1]`, where `action`
represents the numerical torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | hip_1 (front_left_leg) | hinge | torque (N m) |
| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | angle_1 (front_left_leg) | hinge | torque (N m) |
| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | hip_2 (front_right_leg) | hinge | torque (N m) |
| 3 | Torque applied on the rotor between torso/abdomen and the right hip (x-coordinate) | -0.4 | 0.4 | right_hip_x (right_thigh) | hinge | torque (N m) |
| 4 | Torque applied on the rotor between torso/abdomen and the right hip (z-coordinate) | -0.4 | 0.4 | right_hip_z (right_thigh) | hinge | torque (N m) |
| 5 | Torque applied on the rotor between torso/abdomen and the right hip (y-coordinate) | -0.4 | 0.4 | right_hip_y (right_thigh) | hinge | torque (N m) |
| 6 | Torque applied on the rotor between the right hip/thigh and the right shin | -0.4 | 0.4 | right_knee | hinge | torque (N m) |
| 7 | Torque applied on the rotor between torso/abdomen and the left hip (x-coordinate) | -0.4 | 0.4 | left_hip_x (left_thigh) | hinge | torque (N m) |
| 8 | Torque applied on the rotor between torso/abdomen and the left hip (z-coordinate) | -0.4 | 0.4 | left_hip_z (left_thigh) | hinge | torque (N m) |
| 9 | Torque applied on the rotor between torso/abdomen and the left hip (y-coordinate) | -0.4 | 0.4 | left_hip_y (left_thigh) | hinge | torque (N m) |
| 10 | Torque applied on the rotor between the left hip/thigh and the left shin | -0.4 | 0.4 | left_knee | hinge | torque (N m) |
| 11 | Torque applied on the rotor between the torso and right upper arm (coordinate -1) | -0.4 | 0.4 | right_shoulder1 | hinge | torque (N m) |
| 12 | Torque applied on the rotor between the torso and right upper arm (coordinate -2) | -0.4 | 0.4 | right_shoulder2 | hinge | torque (N m) |
| 13 | Torque applied on the rotor between the right upper arm and right lower arm | -0.4 | 0.4 | right_elbow | hinge | torque (N m) |
| 14 | Torque applied on the rotor between the torso and left upper arm (coordinate -1) | -0.4 | 0.4 | left_shoulder1 | hinge | torque (N m) |
| 15 | Torque applied on the rotor between the torso and left upper arm (coordinate -2) | -0.4 | 0.4 | left_shoulder2 | hinge | torque (N m) |
| 16 | Torque applied on the rotor between the left upper arm and left lower arm | -0.4 | 0.4 | left_elbow | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the Humanoid,
followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(376,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 1 | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 2 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 3 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 4 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 5 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 6 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 7 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
| 8 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
| 9 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
| 10 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
| 11 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
| 12 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
| 13 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
| 14 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
| 15 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
| 16 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
| 17 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
| 18 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
| 19 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
| 20 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
| 21 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
| 22 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
| 23 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
| 24 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 25 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 26 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 27 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 28 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 29 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
| 30 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | anglular velocity (rad/s) |
| 31 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | anglular velocity (rad/s) |
| 32 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | aanglular velocity (rad/s) |
| 33 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | anglular velocity (rad/s) |
| 34 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | anglular velocity (rad/s) |
| 35 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | anglular velocity (rad/s) |
| 36 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | anglular velocity (rad/s) |
| 37 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | anglular velocity (rad/s) |
| 38 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | anglular velocity (rad/s) |
| 39 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | anglular velocity (rad/s) |
| 40 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | anglular velocity (rad/s) |
| 41 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | anglular velocity (rad/s) |
| 42 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | anglular velocity (rad/s) |
| 43 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | anglular velocity (rad/s) |
| 44 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | anglular velocity (rad/s) |
| 45 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | anglular velocity (rad/s) |
| 46 | angular velocitty of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | anglular velocity (rad/s) |
Additionally, after all the positional and velocity based values in the table,
the state_space consists of (in order):
- *cinert:* Mass and inertia of a single rigid body relative to the center of mass
(this is an intermediate result of transition). It has shape 14*10 (*nbody * 10*)
and hence adds to another 140 elements in the state space.
- *cvel:* Center of mass based velocity. It has shape 14 * 6 (*nbody * 6*) and hence
adds another 84 elements in the state space
- *qfrc_actuator:* Constraint force generated as the actuator force. This has shape
`(23,)` *(nv * 1)* and hence adds another 23 elements to the state space.
- *cfrc_ext:* This is the center of mass based external force on the body. It has shape
14 * 6 (*nbody * 6*) and hence adds to another 84 elements in the state space.
where *nbody* stands for the number of bodies in the robot and *nv* stands for the number
of degrees of freedom (*= dim(qvel)*)
The (x,y,z) coordinates are translational DOFs while the orientations are rotational
DOFs expressed as quaternions. One can read more about free joints on the
[Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:** There are 47 elements in the table above - giving rise to `(378,)`
elements in the state space. In practice (and Gym implementation), the first two
positional elements are omitted from the state space since the reward function is
calculated based on the x-coordinate value. This value is hidden from the algorithm,
which in turn has to develop an abstract understanding of it from the observed rewards.
Therefore, observation space has shape `(376,)` instead of `(378,)` and the table should not have the first two rows.
**Note:** HumanoidStandup-v4 environment no longer has the following contact forces issue.
If using previous HumanoidStandup versions from v4, there have been reported issues that using a Mujoco-Py version > 2.0 results
in the contact forces always being 0. As such we recommend to use a Mujoco-Py version < 2.0
when using the Humanoid environment if you would like to report results with contact forces
(if contact forces are not used in your experiments, you can use version > 2.0).
### Rewards
The reward consists of three parts:
- *uph_cost*: A reward for moving upward (in an attempt to stand up). This is not a relative
reward which meaures how much upward it has moved from the last timestep, but it is an
absolute reward which measures how much upward the Humanoid has moved overall. It is
measured as *(z coordinate after action - 0)/(atomic timestep)*, where *z coordinate after
action* is index 0 in the state/index 2 in the table, and *atomic timestep* is the time for
one frame of movement even though the simulation has a framerate of 5 (done in order to inflate
rewards a little for fassteer learning).
- *quad_ctrl_cost*: A negative reward for penalising the humanoid if it has too large of
a control force. If there are *nu* actuators/controls, then the control has shape `nu x 1`.
It is measured as *0.1 **x** sum(control<sup>2</sup>)*.
- *quad_impact_cost*: A negative reward for penalising the humanoid if the external
contact force is too large. It is calculated as *min(0.5 * 0.000001 * sum(external
contact force<sup>2</sup>), 10)*.
The total reward returned is ***reward*** *=* *uph_cost + 1 - quad_ctrl_cost - quad_impact_cost*
### Starting State
All observations start in state
(0.0, 0.0, 0.105, 1.0, 0.0 ... 0.0) with a uniform noise in the range of
[-0.01, 0.01] added to the positional and velocity values (values in the table)
for stochasticity. Note that the initial z coordinate is intentionally selected
to be low, thereby indicating a laying down humanoid. The initial orientation is
designed to make it face forward as well.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
### Arguments
No additional arguments are currently supported (in v2 and lower), but
modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('HumanoidStandup-v2')
```
There is no v3 for HumanoidStandup, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "humanoidstandup.xml", 5)
utils.EzPickle.__init__(self)
def _get_obs(self):
data = self.data
return np.concatenate(
[
data.qpos.flat[2:],
data.qvel.flat,
data.cinert.flat,
data.cvel.flat,
data.qfrc_actuator.flat,
data.cfrc_ext.flat,
]
)
def step(self, a):
self.do_simulation(a, self.frame_skip)
pos_after = self.data.qpos[2]
data = self.data
uph_cost = (pos_after - 0) / self.model.opt.timestep
quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum()
quad_impact_cost = 0.5e-6 * np.square(data.cfrc_ext).sum()
quad_impact_cost = min(quad_impact_cost, 10)
reward = uph_cost - quad_ctrl_cost - quad_impact_cost + 1
done = bool(False)
return (
self._get_obs(),
reward,
done,
dict(
reward_linup=uph_cost,
reward_quadctrl=-quad_ctrl_cost,
reward_impact=-quad_impact_cost,
),
)
def reset_model(self):
c = 0.01
self.set_state(
self.init_qpos + self.np_random.uniform(low=-c, high=c, size=self.model.nq),
self.init_qvel
+ self.np_random.uniform(
low=-c,
high=c,
size=self.model.nv,
),
)
return self._get_obs()
def viewer_setup(self):
self.viewer.cam.trackbodyid = 1
self.viewer.cam.distance = self.model.stat.extent * 1.0
self.viewer.cam.lookat[2] = 0.8925
self.viewer.cam.elevation = -20

109
gym/envs/mujoco/inverted_double_pendulum.py
View File

@@ -5,113 +5,10 @@ from gym.envs.mujoco import mujoco_env
class InvertedDoublePendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment originates from control theory and builds on the cartpole
environment based on the work done by Barto, Sutton, and Anderson in
["Neuronlike adaptive elements that can solve difficult learning control problems"](https://ieeexplore.ieee.org/document/6313077),
powered by the Mujoco physics simulator - allowing for more complex experiments
(such as varying the effects of gravity or constraints). This environment involves a cart that can
moved linearly, with a pole fixed on it and a second pole fixed on the other end of the first one
(leaving the second pole as the only one with one free end). The cart can be pushed left or right,
and the goal is to balance the second pole on top of the first pole, which is in turn on top of the
cart, by applying continuous forces on the cart.
### Action Space
The agent take a 1-element vector for actions.
The action space is a continuous `(action)` in `[-1, 1]`, where `action` represents the
numerical force applied to the cart (with magnitude representing the amount of force and
sign representing the direction)
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------|-------------|-------------|----------------------------------|-------|-----------|
| 0 | Force applied on the cart | -1 | 1 | slider | slide | Force (N) |
### Observation Space
The state space consists of positional values of different body parts of the pendulum system,
followed by the velocities of those individual parts (their derivatives) with all the
positions ordered before all the velocities.
The observation is a `ndarray` with shape `(11,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | position of the cart along the linear surface | -Inf | Inf | slider | slide | position (m) |
| 1 | sine of the angle between the cart and the first pole | -Inf | Inf | sin(hinge) | hinge | unitless |
| 2 | sine of the angle between the two poles | -Inf | Inf | sin(hinge2) | hinge | unitless |
| 3 | cosine of the angle between the cart and the first pole | -Inf | Inf | cos(hinge) | hinge | unitless |
| 4 | cosine of the angle between the two poles | -Inf | Inf | cos(hinge2) | hinge | unitless |
| 5 | velocity of the cart | -Inf | Inf | slider | slide | velocity (m/s) |
| 6 | angular velocity of the angle between the cart and the first pole | -Inf | Inf | hinge | hinge | angular velocity (rad/s) |
| 7 | angular velocity of the angle between the two poles | -Inf | Inf | hinge2 | hinge | angular velocity (rad/s) |
| 8 | constraint force - 1 | -Inf | Inf | | | Force (N) |
| 9 | constraint force - 2 | -Inf | Inf | | | Force (N) |
| 10 | constraint force - 3 | -Inf | Inf | | | Force (N) |
There is physical contact between the robots and their environment - and Mujoco
attempts at getting realisitic physics simulations for the possible physical contact
dynamics by aiming for physical accuracy and computational efficiency.
There is one constraint force for contacts for each degree of freedom (3).
The approach and handling of constraints by Mujoco is unique to the simulator
and is based on their research. Once can find more information in their
[*documentation*](https://mujoco.readthedocs.io/en/latest/computation.html)
or in their paper
["Analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo"](https://homes.cs.washington.edu/~todorov/papers/TodorovICRA14.pdf).
### Rewards
The reward consists of two parts:
- *alive_bonus*: The goal is to make the second inverted pendulum stand upright
(within a certain angle limit) as long as possible - as such a reward of +10 is awarded
for each timestep that the second pole is upright.
- *distance_penalty*: This reward is a measure of how far the *tip* of the second pendulum
(the only free end) moves, and it is calculated as
*0.01 * x<sup>2</sup> + (y - 2)<sup>2</sup>*, where *x* is the x-coordinate of the tip
and *y* is the y-coordinate of the tip of the second pole.
- *velocity_penalty*: A negative reward for penalising the agent if it moves too
fast *0.001 * v<sub>1</sub><sup>2</sup> + 0.005 * v<sub>2</sub> <sup>2</sup>*
The total reward returned is ***reward*** *=* *alive_bonus - distance_penalty - velocity_penalty*
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise in the range
of [-0.1, 0.1] added to the positional values (cart position and pole angles) and standard
normal force with a standard deviation of 0.1 added to the velocity values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches 1000 timesteps.
2. Any of the state space values is no longer finite.
3. The y_coordinate of the tip of the second pole *is less than or equal* to 1. The maximum standing height of the system is 1.196 m when all the parts are perpendicularly vertical on top of each other).
### Arguments
No additional arguments are currently supported.
```
env = gym.make('InvertedDoublePendulum-v2')
```
There is no v3 for InvertedPendulum, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum)
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "inverted_double_pendulum.xml", 5)
mujoco_env.MujocoEnv.__init__(
self, "inverted_double_pendulum.xml", 5, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def step(self, action):

156
gym/envs/mujoco/inverted_double_pendulum_v4.py Normal file
View File

@@ -0,0 +1,156 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class InvertedDoublePendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment originates from control theory and builds on the cartpole
environment based on the work done by Barto, Sutton, and Anderson in
["Neuronlike adaptive elements that can solve difficult learning control problems"](https://ieeexplore.ieee.org/document/6313077),
powered by the Mujoco physics simulator - allowing for more complex experiments
(such as varying the effects of gravity or constraints). This environment involves a cart that can
moved linearly, with a pole fixed on it and a second pole fixed on the other end of the first one
(leaving the second pole as the only one with one free end). The cart can be pushed left or right,
and the goal is to balance the second pole on top of the first pole, which is in turn on top of the
cart, by applying continuous forces on the cart.
### Action Space
The agent take a 1-element vector for actions.
The action space is a continuous `(action)` in `[-1, 1]`, where `action` represents the
numerical force applied to the cart (with magnitude representing the amount of force and
sign representing the direction)
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------|-------------|-------------|----------------------------------|-------|-----------|
| 0 | Force applied on the cart | -1 | 1 | slider | slide | Force (N) |
### Observation Space
The state space consists of positional values of different body parts of the pendulum system,
followed by the velocities of those individual parts (their derivatives) with all the
positions ordered before all the velocities.
The observation is a `ndarray` with shape `(11,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | position of the cart along the linear surface | -Inf | Inf | slider | slide | position (m) |
| 1 | sine of the angle between the cart and the first pole | -Inf | Inf | sin(hinge) | hinge | unitless |
| 2 | sine of the angle between the two poles | -Inf | Inf | sin(hinge2) | hinge | unitless |
| 3 | cosine of the angle between the cart and the first pole | -Inf | Inf | cos(hinge) | hinge | unitless |
| 4 | cosine of the angle between the two poles | -Inf | Inf | cos(hinge2) | hinge | unitless |
| 5 | velocity of the cart | -Inf | Inf | slider | slide | velocity (m/s) |
| 6 | angular velocity of the angle between the cart and the first pole | -Inf | Inf | hinge | hinge | angular velocity (rad/s) |
| 7 | angular velocity of the angle between the two poles | -Inf | Inf | hinge2 | hinge | angular velocity (rad/s) |
| 8 | constraint force - 1 | -Inf | Inf | | | Force (N) |
| 9 | constraint force - 2 | -Inf | Inf | | | Force (N) |
| 10 | constraint force - 3 | -Inf | Inf | | | Force (N) |
There is physical contact between the robots and their environment - and Mujoco
attempts at getting realisitic physics simulations for the possible physical contact
dynamics by aiming for physical accuracy and computational efficiency.
There is one constraint force for contacts for each degree of freedom (3).
The approach and handling of constraints by Mujoco is unique to the simulator
and is based on their research. Once can find more information in their
[*documentation*](https://mujoco.readthedocs.io/en/latest/computation.html)
or in their paper
["Analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo"](https://homes.cs.washington.edu/~todorov/papers/TodorovICRA14.pdf).
### Rewards
The reward consists of two parts:
- *alive_bonus*: The goal is to make the second inverted pendulum stand upright
(within a certain angle limit) as long as possible - as such a reward of +10 is awarded
for each timestep that the second pole is upright.
- *distance_penalty*: This reward is a measure of how far the *tip* of the second pendulum
(the only free end) moves, and it is calculated as
*0.01 * x<sup>2</sup> + (y - 2)<sup>2</sup>*, where *x* is the x-coordinate of the tip
and *y* is the y-coordinate of the tip of the second pole.
- *velocity_penalty*: A negative reward for penalising the agent if it moves too
fast *0.001 * v<sub>1</sub><sup>2</sup> + 0.005 * v<sub>2</sub> <sup>2</sup>*
The total reward returned is ***reward*** *=* *alive_bonus - distance_penalty - velocity_penalty*
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise in the range
of [-0.1, 0.1] added to the positional values (cart position and pole angles) and standard
normal force with a standard deviation of 0.1 added to the velocity values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches 1000 timesteps.
2. Any of the state space values is no longer finite.
3. The y_coordinate of the tip of the second pole *is less than or equal* to 1. The maximum standing height of the system is 1.196 m when all the parts are perpendicularly vertical on top of each other).
### Arguments
No additional arguments are currently supported (in v2 and lower), but modifications can
be made to the XML file in the assets folder (or by changing the path to a modified XML
file in another folder)..
```
env = gym.make('InvertedDoublePendulum-v2')
```
There is no v3 for InvertedPendulum, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum)
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "inverted_double_pendulum.xml", 5)
utils.EzPickle.__init__(self)
def step(self, action):
self.do_simulation(action, self.frame_skip)
ob = self._get_obs()
x, _, y = self.data.site_xpos[0]
dist_penalty = 0.01 * x**2 + (y - 2) ** 2
v1, v2 = self.data.qvel[1:3]
vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2
alive_bonus = 10
r = alive_bonus - dist_penalty - vel_penalty
done = bool(y <= 1)
return ob, r, done, {}
def _get_obs(self):
return np.concatenate(
[
self.data.qpos[:1], # cart x pos
np.sin(self.data.qpos[1:]), # link angles
np.cos(self.data.qpos[1:]),
np.clip(self.data.qvel, -10, 10),
np.clip(self.data.qfrc_constraint, -10, 10),
]
).ravel()
def reset_model(self):
self.set_state(
self.init_qpos
+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq),
self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1,
)
return self._get_obs()
def viewer_setup(self):
v = self.viewer
v.cam.trackbodyid = 0
v.cam.distance = self.model.stat.extent * 0.5
v.cam.lookat[2] = 0.12250000000000005 # v.model.stat.center[2]

80
gym/envs/mujoco/inverted_pendulum.py
View File

@@ -5,85 +5,11 @@ from gym.envs.mujoco import mujoco_env
class InvertedPendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is the cartpole environment based on the work done by
Barto, Sutton, and Anderson in ["Neuronlike adaptive elements that can
solve difficult learning control problems"](https://ieeexplore.ieee.org/document/6313077),
just like in the classic environments but now powered by the Mujoco physics simulator -
allowing for more complex experiments (such as varying the effects of gravity).
This environment involves a cart that can moved linearly, with a pole fixed on it
at one end and having another end free. The cart can be pushed left or right, and the
goal is to balance the pole on the top of the cart by applying forces on the cart.
### Action Space
The agent take a 1-element vector for actions.
The action space is a continuous `(action)` in `[-3, 3]`, where `action` represents
the numerical force applied to the cart (with magnitude representing the amount of
force and sign representing the direction)
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------|-------------|-------------|----------------------------------|-------|-----------|
| 0 | Force applied on the cart | -3 | 3 | slider | slide | Force (N) |
### Observation Space
The state space consists of positional values of different body parts of
the pendulum system, followed by the velocities of those individual parts (their derivatives)
with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(4,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | position of the cart along the linear surface | -Inf | Inf | slider | slide | position (m) |
| 1 | vertical angle of the pole on the cart | -Inf | Inf | hinge | hinge | angle (rad) |
| 2 | linear velocity of the cart | -Inf | Inf | slider | slide | velocity (m/s) |
| 3 | angular velocity of the pole on the cart | -Inf | Inf | hinge | hinge | anglular velocity (rad/s) |
### Rewards
The goal is to make the inverted pendulum stand upright (within a certain angle limit)
as long as possible - as such a reward of +1 is awarded for each timestep that
the pole is upright.
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0) with a uniform noise in the range
of [-0.01, 0.01] added to the values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches 1000 timesteps.
2. Any of the state space values is no longer finite.
3. The absolutely value of the vertical angle between the pole and the cart is greater than 0.2 radian.
### Arguments
No additional arguments are currently supported.
```
env = gym.make('InvertedPendulum-v2')
```
There is no v3 for InvertedPendulum, unlike the robot environments where a
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum)
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "inverted_pendulum.xml", 2)
mujoco_env.MujocoEnv.__init__(
self, "inverted_pendulum.xml", 2, mujoco_bindings="mujoco_py"
)
def step(self, a):
reward = 1.0

116
gym/envs/mujoco/inverted_pendulum_v4.py Normal file
View File

@@ -0,0 +1,116 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class InvertedPendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment is the cartpole environment based on the work done by
Barto, Sutton, and Anderson in ["Neuronlike adaptive elements that can
solve difficult learning control problems"](https://ieeexplore.ieee.org/document/6313077),
just like in the classic environments but now powered by the Mujoco physics simulator -
allowing for more complex experiments (such as varying the effects of gravity).
This environment involves a cart that can moved linearly, with a pole fixed on it
at one end and having another end free. The cart can be pushed left or right, and the
goal is to balance the pole on the top of the cart by applying forces on the cart.
### Action Space
The agent take a 1-element vector for actions.
The action space is a continuous `(action)` in `[-3, 3]`, where `action` represents
the numerical force applied to the cart (with magnitude representing the amount of
force and sign representing the direction)
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------|-------------|-------------|----------------------------------|-------|-----------|
| 0 | Force applied on the cart | -3 | 3 | slider | slide | Force (N) |
### Observation Space
The state space consists of positional values of different body parts of
the pendulum system, followed by the velocities of those individual parts (their derivatives)
with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(4,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | position of the cart along the linear surface | -Inf | Inf | slider | slide | position (m) |
| 1 | vertical angle of the pole on the cart | -Inf | Inf | hinge | hinge | angle (rad) |
| 2 | linear velocity of the cart | -Inf | Inf | slider | slide | velocity (m/s) |
| 3 | angular velocity of the pole on the cart | -Inf | Inf | hinge | hinge | anglular velocity (rad/s) |
### Rewards
The goal is to make the inverted pendulum stand upright (within a certain angle limit)
as long as possible - as such a reward of +1 is awarded for each timestep that
the pole is upright.
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0) with a uniform noise in the range
of [-0.01, 0.01] added to the values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches 1000 timesteps.
2. Any of the state space values is no longer finite.
3. The absolutely value of the vertical angle between the pole and the cart is greater than 0.2 radian.
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder).
```
env = gym.make('InvertedPendulum-v2')
```
There is no v3 for InvertedPendulum, unlike the robot environments where a
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (including inverted pendulum)
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "inverted_pendulum.xml", 2)
def step(self, a):
reward = 1.0
self.do_simulation(a, self.frame_skip)
ob = self._get_obs()
notdone = np.isfinite(ob).all() and (np.abs(ob[1]) <= 0.2)
done = not notdone
return ob, reward, done, {}
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(
size=self.model.nq, low=-0.01, high=0.01
)
qvel = self.init_qvel + self.np_random.uniform(
size=self.model.nv, low=-0.01, high=0.01
)
self.set_state(qpos, qvel)
return self._get_obs()
def _get_obs(self):
return np.concatenate([self.data.qpos, self.data.qvel]).ravel()
def viewer_setup(self):
v = self.viewer
v.cam.trackbodyid = 0
v.cam.distance = self.model.stat.extent

138
gym/envs/mujoco/mujoco_env.py
View File

@@ -1,4 +1,3 @@
import os
from collections import OrderedDict
from os import path
from typing import Optional
@@ -6,18 +5,9 @@ from typing import Optional
import numpy as np
import gym
from gym import error, spaces
from gym import error, logger, spaces
try:
import mujoco_py
except ImportError as e:
raise error.DependencyNotInstalled(
"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(
e
)
)
DEFAULT_SIZE = 500
DEFAULT_SIZE = 480
def convert_observation_to_space(observation):
@@ -43,28 +33,64 @@ def convert_observation_to_space(observation):
class MujocoEnv(gym.Env):
"""Superclass for all MuJoCo environments."""
def __init__(self, model_path, frame_skip):
def __init__(self, model_path, frame_skip, mujoco_bindings="mujoco"):
if model_path.startswith("/"):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), "assets", model_path)
fullpath = path.join(path.dirname(__file__), "assets", model_path)
if not path.exists(fullpath):
raise OSError(f"File {fullpath} does not exist")
self.frame_skip = frame_skip
self.model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(self.model)
if mujoco_bindings == "mujoco_py":
logger.warn(
"This version of the mujoco environments depends "
"on the mujoco-py bindings, which are no longer maintained "
"and may stop working. Please upgrade to the v4 versions of "
"the environments (which depend on the mujoco python bindings instead), unless "
"you are trying to precisely replicate previous works)."
)
try:
import mujoco_py
self._mujoco_bindings = mujoco_py
except ImportError as e:
raise error.DependencyNotInstalled(
"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(
e
)
)
self.model = self._mujoco_bindings.load_model_from_path(fullpath)
self.sim = self._mujoco_bindings.MjSim(self.model)
self.data = self.sim.data
self.viewer = None
elif mujoco_bindings == "mujoco":
try:
import mujoco
self._mujoco_bindings = mujoco
except ImportError as e:
raise error.DependencyNotInstalled(
f"{e}. (HINT: you need to install mujoco)"
)
self.model = self._mujoco_bindings.MjModel.from_xml_path(fullpath)
self.data = self._mujoco_bindings.MjData(self.model)
self.init_qpos = self.data.qpos.ravel().copy()
self.init_qvel = self.data.qvel.ravel().copy()
self._viewers = {}
self.frame_skip = frame_skip
self.viewer = None
self.metadata = {
"render_modes": ["human", "rgb_array", "depth_array"],
"render_fps": int(np.round(1.0 / self.dt)),
}
self.init_qpos = self.sim.data.qpos.ravel().copy()
self.init_qvel = self.sim.data.qvel.ravel().copy()
self._set_action_space()
action = self.action_space.sample()
@@ -111,7 +137,12 @@ class MujocoEnv(gym.Env):
options: Optional[dict] = None,
):
super().reset(seed=seed)
if self._mujoco_bindings.__name__ == "mujoco_py":
self.sim.reset()
else:
self._mujoco_bindings.mj_resetData(self.model, self.data)
ob = self.reset_model()
if not return_info:
return ob
@@ -120,12 +151,19 @@ class MujocoEnv(gym.Env):
def set_state(self, qpos, qvel):
assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
old_state = self.sim.get_state()
new_state = mujoco_py.MjSimState(
old_state.time, qpos, qvel, old_state.act, old_state.udd_state
if self._mujoco_bindings.__name__ == "mujoco_py":
state = self.sim.get_state()
state = self._mujoco_bindings.MjSimState(
state.time, qpos, qvel, state.act, state.udd_state
)
self.sim.set_state(new_state)
self.sim.set_state(state)
self.sim.forward()
else:
self.data.qpos[:] = np.copy(qpos)
self.data.qvel[:] = np.copy(qvel)
if self.model.na == 0:
self.data.act[:] = None
self._mujoco_bindings.mj_forward(self.model, self.data)
@property
def dt(self):
@@ -135,9 +173,21 @@ class MujocoEnv(gym.Env):
if np.array(ctrl).shape != self.action_space.shape:
raise ValueError("Action dimension mismatch")
if self._mujoco_bindings.__name__ == "mujoco_py":
self.sim.data.ctrl[:] = ctrl
else:
self.data.ctrl[:] = ctrl
for _ in range(n_frames):
if self._mujoco_bindings.__name__ == "mujoco_py":
self.sim.step()
else:
self._mujoco_bindings.mj_step(self.model, self.data)
# As of MuJoCo 2.0, force-related quantities like cacc are not computed
# unless there's a force sensor in the model.
# See https://github.com/openai/gym/issues/1541
if self._mujoco_bindings.__name__ != "mujoco_py":
self._mujoco_bindings.mj_rnePostConstraint(self.model, self.data)
def render(
self,
@@ -158,19 +208,25 @@ class MujocoEnv(gym.Env):
if no_camera_specified:
camera_name = "track"
if camera_id is None and camera_name in self.model._camera_name2id:
if camera_id is None:
if self._mujoco_bindings.__name__ == "mujoco_py":
if camera_name in self.model._camera_name2id:
camera_id = self.model.camera_name2id(camera_name)
else:
camera_id = self._mujoco_bindings.mj_name2id(
self.model,
self._mujoco_bindings.mjtObj.mjOBJ_CAMERA,
camera_name,
)
self._get_viewer(mode).render(width, height, camera_id=camera_id)
if mode == "rgb_array":
# window size used for old mujoco-py:
data = self._get_viewer(mode).read_pixels(width, height, depth=False)
# original image is upside-down, so flip it
return data[::-1, :, :]
elif mode == "depth_array":
self._get_viewer(mode).render(width, height)
# window size used for old mujoco-py:
# Extract depth part of the read_pixels() tuple
data = self._get_viewer(mode).read_pixels(width, height, depth=True)[1]
# original image is upside-down, so flip it
@@ -180,24 +236,40 @@ class MujocoEnv(gym.Env):
def close(self):
if self.viewer is not None:
# self.viewer.finish()
self.viewer = None
self._viewers = {}
def _get_viewer(self, mode):
def _get_viewer(self, mode, width=DEFAULT_SIZE, height=DEFAULT_SIZE):
self.viewer = self._viewers.get(mode)
if self.viewer is None:
if mode == "human":
self.viewer = mujoco_py.MjViewer(self.sim)
if self._mujoco_bindings.__name__ == "mujoco_py":
self.viewer = self._mujoco_bindings.MjViewer(self.sim)
else:
from gym.envs.mujoco.mujoco_rendering import Viewer
self.viewer = Viewer(self.model, self.data)
elif mode == "rgb_array" or mode == "depth_array":
self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, -1)
if self._mujoco_bindings.__name__ == "mujoco_py":
self.viewer = self._mujoco_bindings.MjRenderContextOffscreen(
self.sim, -1
)
else:
from gym.envs.mujoco.mujoco_rendering import RenderContextOffscreen
self.viewer = RenderContextOffscreen(
width, height, self.model, self.data
)
self.viewer_setup()
self._viewers[mode] = self.viewer
return self.viewer
def get_body_com(self, body_name):
if self._mujoco_bindings.__name__ == "mujoco_py":
return self.data.get_body_xpos(body_name)
else:
return self.data.body(body_name).xpos
def state_vector(self):
return np.concatenate([self.sim.data.qpos.flat, self.sim.data.qvel.flat])
return np.concatenate([self.data.qpos.flat, self.data.qvel.flat])

554
gym/envs/mujoco/mujoco_rendering.py Normal file
View File

@@ -0,0 +1,554 @@
import collections
import os
import sys
import time
from threading import Lock
import glfw
import imageio
import mujoco
import numpy as np
def _import_egl(width, height):
from mujoco.egl import GLContext
return GLContext(width, height)
def _import_glfw(width, height):
from mujoco.glfw import GLContext
return GLContext(width, height)
def _import_osmesa(width, height):
from mujoco.osmesa import GLContext
return GLContext(width, height)
_ALL_RENDERERS = collections.OrderedDict(
[
("glfw", _import_glfw),
("egl", _import_egl),
("osmesa", _import_osmesa),
]
)
class RenderContext:
"""Render context superclass for offscreen and window rendering."""
def __init__(self, model, data, offscreen=True):
self.model = model
self.data = data
self.offscreen = offscreen
max_geom = 1000
mujoco.mj_forward(self.model, self.data)
self.scn = mujoco.MjvScene(self.model, max_geom)
self.cam = mujoco.MjvCamera()
self.vopt = mujoco.MjvOption()
self.pert = mujoco.MjvPerturb()
self.con = mujoco.MjrContext(self.model, mujoco.mjtFontScale.mjFONTSCALE_150)
self._markers = []
self._overlays = {}
self._init_camera()
self._set_mujoco_buffers()
def _set_mujoco_buffers(self):
if self.offscreen:
mujoco.mjr_setBuffer(mujoco.mjtFramebuffer.mjFB_OFFSCREEN, self.con)
if self.con.currentBuffer != mujoco.mjtFramebuffer.mjFB_OFFSCREEN:
raise RuntimeError("Offscreen rendering not supported")
else:
mujoco.mjr_setBuffer(mujoco.mjtFramebuffer.mjFB_WINDOW, self.con)
if self.con.currentBuffer != mujoco.mjtFramebuffer.mjFB_WINDOW:
raise RuntimeError("Window rendering not supported")
def update_offscreen_size(self, width, height):
if width != self.con.offWidth or height != self.con.offHeight:
self.model.vis.global_.offwidth = width
self.model.vis.global_.offheight = height
self.con.free()
self._set_mujoco_buffers()
def render(self, width, height, camera_id=None, segmentation=False):
rect = mujoco.MjrRect(left=0, bottom=0, width=width, height=height)
# Sometimes buffers are too small.
if width > self.con.offWidth or height > self.con.offHeight:
new_width = max(width, self.model.vis.global_.offwidth)
new_height = max(height, self.model.vis.global_.offheight)
self.update_offscreen_size(new_width, new_height)
if camera_id is not None:
if camera_id == -1:
self.cam.type = mujoco.mjtCamera.mjCAMERA_FREE
else:
self.cam.type = mujoco.mjtCamera.mjCAMERA_FIXED
self.cam.fixedcamid = camera_id
mujoco.mjv_updateScene(
self.model,
self.data,
self.vopt,
self.pert,
self.cam,
mujoco.mjtCatBit.mjCAT_ALL,
self.scn,
)
if segmentation:
self.scn.flags[mujoco.mjtRndFlag.mjRND_SEGMENT] = 1
self.scn.flags[mujoco.mjtRndFlag.mjRND_IDCOLOR] = 1
for marker_params in self._markers:
self._add_marker_to_scene(marker_params)
mujoco.mjr_render(rect, self.scn, self.con)
for gridpos, (text1, text2) in self._overlays.items():
mujoco.mjr_overlay(
mujoco.mjtFontScale.mjFONTSCALE_150,
gridpos,
rect,
text1.encode(),
text2.encode(),
self.con,
)
if segmentation:
self.scn.flags[mujoco.mjtRndFlag.mjRND_SEGMENT] = 0
self.scn.flags[mujoco.mjtRndFlag.mjRND_IDCOLOR] = 0
def read_pixels(self, width, height, depth=True, segmentation=False):
rect = mujoco.MjrRect(left=0, bottom=0, width=width, height=height)
rgb_arr = np.zeros(3 * rect.width * rect.height, dtype=np.uint8)
depth_arr = np.zeros(rect.width * rect.height, dtype=np.float32)
mujoco.mjr_readPixels(rgb_arr, depth_arr, rect, self.con)
rgb_img = rgb_arr.reshape(rect.height, rect.width, 3)
ret_img = rgb_img
if segmentation:
seg_img = (
rgb_img[:, :, 0]
+ rgb_img[:, :, 1] * (2**8)
+ rgb_img[:, :, 2] * (2**16)
)
seg_img[seg_img >= (self.scn.ngeom + 1)] = 0
seg_ids = np.full((self.scn.ngeom + 1, 2), fill_value=-1, dtype=np.int32)
for i in range(self.scn.ngeom):
geom = self.scn.geoms[i]
if geom.segid != -1:
seg_ids[geom.segid + 1, 0] = geom.objtype
seg_ids[geom.segid + 1, 1] = geom.objid
ret_img = seg_ids[seg_img]
if depth:
depth_img = depth_arr.reshape(rect.height, rect.width)
return (ret_img, depth_img)
else:
return ret_img
def _init_camera(self):
self.cam.type = mujoco.mjtCamera.mjCAMERA_FREE
self.cam.fixedcamid = -1
for i in range(3):
self.cam.lookat[i] = np.median(self.data.geom_xpos[:, i])
self.cam.distance = self.model.stat.extent
def add_overlay(self, gridpos: int, text1: str, text2: str):
"""Overlays text on the scene."""
if gridpos not in self._overlays:
self._overlays[gridpos] = ["", ""]
self._overlays[gridpos][0] += text1 + "\n"
self._overlays[gridpos][1] += text2 + "\n"
def add_marker(self, **marker_params):
self._markers.append(marker_params)
def _add_marker_to_scene(self, marker):
if self.scn.ngeom >= self.scn.maxgeom:
raise RuntimeError("Ran out of geoms. maxgeom: %d" % self.scn.maxgeom)
g = self.scn.geoms[self.scn.ngeom]
# default values.
g.dataid = -1
g.objtype = mujoco.mjtObj.mjOBJ_UNKNOWN
g.objid = -1
g.category = mujoco.mjtCatBit.mjCAT_DECOR
g.texid = -1
g.texuniform = 0
g.texrepeat[0] = 1
g.texrepeat[1] = 1
g.emission = 0
g.specular = 0.5
g.shininess = 0.5
g.reflectance = 0
g.type = mujoco.mjtGeom.mjGEOM_BOX
g.size[:] = np.ones(3) * 0.1
g.mat[:] = np.eye(3)
g.rgba[:] = np.ones(4)
for key, value in marker.items():
if isinstance(value, (int, float, mujoco._enums.mjtGeom)):
setattr(g, key, value)
elif isinstance(value, (tuple, list, np.ndarray)):
attr = getattr(g, key)
attr[:] = np.asarray(value).reshape(attr.shape)
elif isinstance(value, str):
assert key == "label", "Only label is a string in mjtGeom."
if value is None:
g.label[0] = 0
else:
g.label = value
elif hasattr(g, key):
raise ValueError(
"mjtGeom has attr {} but type {} is invalid".format(
key, type(value)
)
)
else:
raise ValueError("mjtGeom doesn't have field %s" % key)
self.scn.ngeom += 1
class RenderContextOffscreen(RenderContext):
"""Offscreen rendering class with opengl context."""
def __init__(self, width, height, model, data):
self._get_opengl_backend(width, height)
self.opengl_context.make_current()
super().__init__(model, data, offscreen=True)
def _get_opengl_backend(self, width, height):
backend = os.environ.get("MUJOCO_GL")
if backend is not None:
try:
self.opengl_context = _ALL_RENDERERS[backend](width, height)
except KeyError:
raise RuntimeError(
"Environment variable {} must be one of {!r}: got {!r}.".format(
"MUJOCO_GL", _ALL_RENDERERS.keys(), backend
)
)
else:
for name, _ in _ALL_RENDERERS.items():
try:
self.opengl_context = _ALL_RENDERERS[name](width, height)
backend = name
break
except: # noqa:E722
pass
if backend is None:
raise RuntimeError(
"No OpenGL backend could be imported. Attempting to create a "
"rendering context will result in a RuntimeError."
)
class Viewer(RenderContext):
"""Class for window rendering in all MuJoCo environments."""
def __init__(self, model, data):
self._gui_lock = Lock()
self._button_left_pressed = False
self._button_right_pressed = False
self._last_mouse_x = 0
self._last_mouse_y = 0
self._paused = False
self._transparent = False
self._contacts = False
self._render_every_frame = True
self._image_idx = 0
self._image_path = "/tmp/frame_%07d.png"
self._time_per_render = 1 / 60.0
self._run_speed = 1.0
self._loop_count = 0
self._advance_by_one_step = False
self._hide_menu = False
# glfw init
glfw.init()
width, height = glfw.get_video_mode(glfw.get_primary_monitor()).size
self.window = glfw.create_window(width // 2, height // 2, "mujoco", None, None)
glfw.make_context_current(self.window)
glfw.swap_interval(1)
framebuffer_width, framebuffer_height = glfw.get_framebuffer_size(self.window)
window_width, _ = glfw.get_window_size(self.window)
self._scale = framebuffer_width * 1.0 / window_width
# set callbacks
glfw.set_cursor_pos_callback(self.window, self._cursor_pos_callback)
glfw.set_mouse_button_callback(self.window, self._mouse_button_callback)
glfw.set_scroll_callback(self.window, self._scroll_callback)
glfw.set_key_callback(self.window, self._key_callback)
# get viewport
self.viewport = mujoco.MjrRect(0, 0, framebuffer_width, framebuffer_height)
super().__init__(model, data, offscreen=False)
def _key_callback(self, key, action):
if action != glfw.RELEASE:
return
# Switch cameras
elif key == glfw.KEY_TAB:
self.cam.fixedcamid += 1
self.cam.type = mujoco.mjtCamera.mjCAMERA_FIXED
if self.cam.fixedcamid >= self.model.ncam:
self.cam.fixedcamid = -1
self.cam.type = mujoco.mjtCamera.mjCAMERA_FREE
# Pause simulation
elif key == glfw.KEY_SPACE and self._paused is not None:
self._paused = not self._paused
# Advances simulation by one step.
elif key == glfw.KEY_RIGHT and self._paused is not None:
self._advance_by_one_step = True
self._paused = True
# Slows down simulation
elif key == glfw.KEY_S:
self._run_speed /= 2.0
# Speeds up simulation
elif key == glfw.KEY_F:
self._run_speed *= 2.0
# Turn off / turn on rendering every frame.
elif key == glfw.KEY_D:
self._render_every_frame = not self._render_every_frame
# Capture screenshot
elif key == glfw.KEY_T:
img = np.zeros(
(
glfw.get_framebuffer_size(self.window)[1],
glfw.get_framebuffer_size(self.window)[0],
3,
),
dtype=np.uint8,
)
mujoco.mjr_readPixels(img, None, self.viewport, self.con)
imageio.imwrite(self._image_path % self._image_idx, np.flipud(img))
self._image_idx += 1
# Display contact forces
elif key == glfw.KEY_C:
self._contacts = not self._contacts
self.vopt.flags[mujoco.mjtVisFlag.mjVIS_CONTACTPOINT] = self._contacts
self.vopt.flags[mujoco.mjtVisFlag.mjVIS_CONTACTFORCE] = self._contacts
# Display coordinate frames
elif key == glfw.KEY_E:
self.vopt.frame = 1 - self.vopt.frame
# Hide overlay menu
elif key == glfw.KEY_H:
self._hide_menu = not self._hide_menu
# Make transparent
elif key == glfw.KEY_R:
self._transparent = not self._transparent
if self._transparent:
self.model.geom_rgba[:, 3] /= 5.0
else:
self.model.geom_rgba[:, 3] *= 5.0
# Geom group visibility
elif key in (glfw.KEY_0, glfw.KEY_1, glfw.KEY_2, glfw.KEY_3, glfw.KEY_4):
self.vopt.geomgroup[key - glfw.KEY_0] ^= 1
# Quit
if key == glfw.KEY_ESCAPE:
print("Pressed ESC")
print("Quitting.")
glfw.terminate()
sys.exit(0)
def _cursor_pos_callback(self, window, xpos, ypos):
if not (self._button_left_pressed or self._button_right_pressed):
return
mod_shift = (
glfw.get_key(window, glfw.KEY_LEFT_SHIFT) == glfw.PRESS
or glfw.get_key(window, glfw.KEY_RIGHT_SHIFT) == glfw.PRESS
)
if self._button_right_pressed:
action = (
mujoco.mjtMouse.mjMOUSE_MOVE_H
if mod_shift
else mujoco.mjtMouse.mjMOUSE_MOVE_V
)
elif self._button_left_pressed:
action = (
mujoco.mjtMouse.mjMOUSE_ROTATE_H
if mod_shift
else mujoco.mjtMouse.mjMOUSE_ROTATE_V
)
else:
action = mujoco.mjtMouse.mjMOUSE_ZOOM
dx = int(self._scale * xpos) - self._last_mouse_x
dy = int(self._scale * ypos) - self._last_mouse_y
width, height = glfw.get_framebuffer_size(window)
with self._gui_lock:
mujoco.mjv_moveCamera(
self.model, action, dx / height, dy / height, self.scn, self.cam
)
self._last_mouse_x = int(self._scale * xpos)
self._last_mouse_y = int(self._scale * ypos)
def _mouse_button_callback(self, window, button, act, mods):
self._button_left_pressed = (
glfw.get_mouse_button(window, glfw.MOUSE_BUTTON_LEFT) == glfw.PRESS
)
self._button_right_pressed = (
glfw.get_mouse_button(window, glfw.MOUSE_BUTTON_RIGHT) == glfw.PRESS
)
x, y = glfw.get_cursor_pos(window)
self._last_mouse_x = int(self._scale * x)
self._last_mouse_y = int(self._scale * y)
def _scroll_callback(self, window, x_offset, y_offset):
with self._gui_lock:
mujoco.mjv_moveCamera(
self.model,
mujoco.mjtMouse.mjMOUSE_ZOOM,
0,
-0.05 * y_offset,
self.scn,
self.cam,
)
def _create_overlay(self):
topleft = mujoco.mjtGridPos.mjGRID_TOPLEFT
bottomleft = mujoco.mjtGridPos.mjGRID_BOTTOMLEFT
if self._render_every_frame:
self.add_overlay(topleft, "", "")
else:
self.add_overlay(
topleft,
"Run speed = %.3f x real time" % self._run_speed,
"[S]lower, [F]aster",
)
self.add_overlay(
topleft, "Ren[d]er every frame", "On" if self._render_every_frame else "Off"
)
self.add_overlay(
topleft,
"Switch camera (#cams = %d)" % (self.model.ncam + 1),
"[Tab] (camera ID = %d)" % self.cam.fixedcamid,
)
self.add_overlay(topleft, "[C]ontact forces", "On" if self._contacts else "Off")
self.add_overlay(topleft, "T[r]ansparent", "On" if self._transparent else "Off")
if self._paused is not None:
if not self._paused:
self.add_overlay(topleft, "Stop", "[Space]")
else:
self.add_overlay(topleft, "Start", "[Space]")
self.add_overlay(
topleft, "Advance simulation by one step", "[right arrow]"
)
self.add_overlay(
topleft, "Referenc[e] frames", "On" if self.vopt.frame == 1 else "Off"
)
self.add_overlay(topleft, "[H]ide Menu", "")
if self._image_idx > 0:
fname = self._image_path % (self._image_idx - 1)
self.add_overlay(topleft, "Cap[t]ure frame", "Saved as %s" % fname)
else:
self.add_overlay(topleft, "Cap[t]ure frame", "")
self.add_overlay(topleft, "Toggle geomgroup visibility", "0-4")
self.add_overlay(bottomleft, "FPS", "%d%s" % (1 / self._time_per_render, ""))
self.add_overlay(
bottomleft, "Solver iterations", str(self.data.solver_iter + 1)
)
self.add_overlay(
bottomleft, "Step", str(round(self.data.time / self.model.opt.timestep))
)
self.add_overlay(bottomleft, "timestep", "%.5f" % self.model.opt.timestep)
def render(self):
# mjv_updateScene, mjr_render, mjr_overlay
def update():
# fill overlay items
self._create_overlay()
render_start = time.time()
if self.window is None:
return
elif glfw.window_should_close(self.window):
glfw.terminate()
sys.exit(0)
self.viewport.width, self.viewport.height = glfw.get_framebuffer_size(
self.window
)
with self._gui_lock:
# update scene
mujoco.mjv_updateScene(
self.model,
self.data,
self.vopt,
mujoco.MjvPerturb(),
self.cam,
mujoco.mjtCatBit.mjCAT_ALL.value,
self.scn,
)
# marker items
for marker in self._markers:
self._add_marker_to_scene(marker)
# render
mujoco.mjr_render(self.viewport, self.scn, self.con)
# overlay items
if not self._hide_menu:
for gridpos, [t1, t2] in self._overlays.items():
mujoco.mjr_overlay(
mujoco.mjtFontScale.mjFONTSCALE_150,
gridpos,
self.viewport,
t1,
t2,
self.con,
)
glfw.swap_buffers(self.window)
glfw.poll_events()
self._time_per_render = 0.9 * self._time_per_render + 0.1 * (
time.time() - render_start
)
# clear overlay
self._overlays.clear()
if self._paused:
while self._paused:
update()
if self._advance_by_one_step:
self._advance_by_one_step = False
break
else:
self._loop_count += self.model.opt.timestep / (
self._time_per_render * self._run_speed
)
if self._render_every_frame:
self._loop_count = 1
while self._loop_count > 0:
update()
self._loop_count -= 1
# clear markers
self._markers[:] = []
def close(self):
glfw.terminate()
sys.exit(0)

126
gym/envs/mujoco/pusher.py
View File

@@ -5,131 +5,11 @@ from gym.envs.mujoco import mujoco_env
class PusherEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
"Pusher" is a multi-jointed robot arm which is very similar to that of a human.
The goal is to move a target cylinder (called *object*) to a goal position using the robot's end effector (called *fingertip*).
The robot consists of shoulder, elbow, forearm, and wrist joints.
### Action Space
The action space is a `Box(-2, 2, (7,), float32)`. An action `(a, b)` represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|--------------------------------------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Rotation of the panning the shoulder | -2 | 2 | r_shoulder_pan_joint | hinge | torque (N m) |
| 1 | Rotation of the shoulder lifting joint | -2 | 2 | r_shoulder_lift_joint | hinge | torque (N m) |
| 2 | Rotation of the shoulder rolling joint | -2 | 2 | r_upper_arm_roll_joint | hinge | torque (N m) |
| 3 | Rotation of hinge joint that flexed the elbow | -2 | 2 | r_elbow_flex_joint | hinge | torque (N m) |
| 4 | Rotation of hinge that rolls the forearm | -2 | 2 | r_forearm_roll_joint | hinge | torque (N m) |
| 5 | Rotation of flexing the wrist | -2 | 2 | r_wrist_flex_joint | hinge | torque (N m) |
| 6 | Rotation of rolling the wrist | -2 | 2 | r_wrist_roll_joint | hinge | torque (N m) |
### Observation Space
Observations consist of
- Angle of rotational joints on the pusher
- Anglular velocities of rotational joints on the pusher
- The coordinates of the fingertip of the pusher
- The coordinates of the object to be moved
- The coordinates of the goal position
The observation is a `ndarray` with shape `(23,)` where the elements correspond to the table below.
An analogy can be drawn to a human arm in order to help understand the state space, with the words flex and roll meaning the
same as human joints.
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | Rotation of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angle (rad) |
| 1 | Rotation of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angle (rad) |
| 2 | Rotation of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angle (rad) |
| 3 | Rotation of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angle (rad) |
| 4 | Rotation of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angle (rad) |
| 5 | Rotation of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angle (rad) |
| 6 | Rotation of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angle (rad) |
| 7 | Rotational velocity of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angular velocity (rad/s) |
| 8 | Rotational velocity of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angular velocity (rad/s) |
| 9 | Rotational velocity of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angular velocity (rad/s) |
| 10 | Rotational velocity of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angular velocity (rad/s) |
| 11 | Rotational velocity of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angular velocity (rad/s) |
| 12 | Rotational velocity of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angular velocity (rad/s) |
| 13 | Rotational velocity of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angular velocity (rad/s) |
| 14 | x-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 15 | y-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 16 | z-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 17 | x-coordinate of the object to be moved | -Inf | Inf | object (obj_slidex) | slide | position (m) |
| 18 | y-coordinate of the object to be moved | -Inf | Inf | object (obj_slidey) | slide | position (m) |
| 19 | z-coordinate of the object to be moved | -Inf | Inf | object | cylinder | position (m) |
| 20 | x-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidex) | slide | position (m) |
| 21 | y-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidey) | slide | position (m) |
| 22 | z-coordinate of the goal position of the object | -Inf | Inf | goal | sphere | position (m) |
### Rewards
The reward consists of two parts:
- *reward_near *: This reward is a measure of how far the *fingertip*
of the pusher (the unattached end) is from the object, with a more negative
value assigned for when the pusher's *fingertip* is further away from the
target. It is calculated as the negative vector norm of (position of
the fingertip - position of target), or *-norm("fingertip" - "target")*.
- *reward_dist *: This reward is a measure of how far the object is from
the target goal position, with a more negative value assigned for object is
further away from the target. It is calculated as the negative vector norm of
(position of the object - position of goal), or *-norm("object" - "target")*.
- *reward_control*: A negative reward for penalising the pusher if
it takes actions that are too large. It is measured as the negative squared
Euclidean norm of the action, i.e. as *- sum(action<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near*
Unlike other environments, Pusher does not allow you to specify weights for the individual reward terms.
However, `info` does contain the keys *reward_dist* and *reward_ctrl*. Thus, if you'd like to weight the terms,
you should create a wrapper that computes the weighted reward from `info`.
### Starting State
All pusher (not including object and goal) states start in
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0). A uniform noise in the range
[-0.005, 0.005] is added to the velocity attributes only. The velocities of
the object and goal are permanently set to 0. The object's x-position is selected uniformly
between [-0.3, 0] while the y-position is selected uniformly between [-0.2, 0.2], and this
process is repeated until the vector norm between the object's (x,y) position and origin is not greater
than 0.17. The goal always have the same position of (0.45, -0.05, -0.323).
The default framerate is 5 with each frame lasting for 0.01, giving rise to a *dt = 5 * 0.01 = 0.05*
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 100 timesteps.
2. Any of the state space values is no longer finite.
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('Pusher-v2')
```
There is no v3 for Pusher, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "pusher.xml", 5)
mujoco_env.MujocoEnv.__init__(
self, "pusher.xml", 5, mujoco_bindings="mujoco_py"
)
def step(self, a):
vec_1 = self.get_body_com("object") - self.get_body_com("tips_arm")

185
gym/envs/mujoco/pusher_v4.py Normal file
View File

@@ -0,0 +1,185 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class PusherEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
"Pusher" is a multi-jointed robot arm which is very similar to that of a human.
The goal is to move a target cylinder (called *object*) to a goal position using the robot's end effector (called *fingertip*).
The robot consists of shoulder, elbow, forearm, and wrist joints.
### Action Space
The action space is a `Box(-2, 2, (7,), float32)`. An action `(a, b)` represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|--------------------------------------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Rotation of the panning the shoulder | -2 | 2 | r_shoulder_pan_joint | hinge | torque (N m) |
| 1 | Rotation of the shoulder lifting joint | -2 | 2 | r_shoulder_lift_joint | hinge | torque (N m) |
| 2 | Rotation of the shoulder rolling joint | -2 | 2 | r_upper_arm_roll_joint | hinge | torque (N m) |
| 3 | Rotation of hinge joint that flexed the elbow | -2 | 2 | r_elbow_flex_joint | hinge | torque (N m) |
| 4 | Rotation of hinge that rolls the forearm | -2 | 2 | r_forearm_roll_joint | hinge | torque (N m) |
| 5 | Rotation of flexing the wrist | -2 | 2 | r_wrist_flex_joint | hinge | torque (N m) |
| 6 | Rotation of rolling the wrist | -2 | 2 | r_wrist_roll_joint | hinge | torque (N m) |
### Observation Space
Observations consist of
- Angle of rotational joints on the pusher
- Anglular velocities of rotational joints on the pusher
- The coordinates of the fingertip of the pusher
- The coordinates of the object to be moved
- The coordinates of the goal position
The observation is a `ndarray` with shape `(23,)` where the elements correspond to the table below.
An analogy can be drawn to a human arm in order to help understand the state space, with the words flex and roll meaning the
same as human joints.
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | Rotation of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angle (rad) |
| 1 | Rotation of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angle (rad) |
| 2 | Rotation of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angle (rad) |
| 3 | Rotation of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angle (rad) |
| 4 | Rotation of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angle (rad) |
| 5 | Rotation of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angle (rad) |
| 6 | Rotation of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angle (rad) |
| 7 | Rotational velocity of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angular velocity (rad/s) |
| 8 | Rotational velocity of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angular velocity (rad/s) |
| 9 | Rotational velocity of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angular velocity (rad/s) |
| 10 | Rotational velocity of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angular velocity (rad/s) |
| 11 | Rotational velocity of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angular velocity (rad/s) |
| 12 | Rotational velocity of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angular velocity (rad/s) |
| 13 | Rotational velocity of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angular velocity (rad/s) |
| 14 | x-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 15 | y-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 16 | z-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) |
| 17 | x-coordinate of the object to be moved | -Inf | Inf | object (obj_slidex) | slide | position (m) |
| 18 | y-coordinate of the object to be moved | -Inf | Inf | object (obj_slidey) | slide | position (m) |
| 19 | z-coordinate of the object to be moved | -Inf | Inf | object | cylinder | position (m) |
| 20 | x-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidex) | slide | position (m) |
| 21 | y-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidey) | slide | position (m) |
| 22 | z-coordinate of the goal position of the object | -Inf | Inf | goal | sphere | position (m) |
### Rewards
The reward consists of two parts:
- *reward_near *: This reward is a measure of how far the *fingertip*
of the pusher (the unattached end) is from the object, with a more negative
value assigned for when the pusher's *fingertip* is further away from the
target. It is calculated as the negative vector norm of (position of
the fingertip - position of target), or *-norm("fingertip" - "target")*.
- *reward_dist *: This reward is a measure of how far the object is from
the target goal position, with a more negative value assigned for object is
further away from the target. It is calculated as the negative vector norm of
(position of the object - position of goal), or *-norm("object" - "target")*.
- *reward_control*: A negative reward for penalising the pusher if
it takes actions that are too large. It is measured as the negative squared
Euclidean norm of the action, i.e. as *- sum(action<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near*
Unlike other environments, Pusher does not allow you to specify weights for the individual reward terms.
However, `info` does contain the keys *reward_dist* and *reward_ctrl*. Thus, if you'd like to weight the terms,
you should create a wrapper that computes the weighted reward from `info`.
### Starting State
All pusher (not including object and goal) states start in
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0). A uniform noise in the range
[-0.005, 0.005] is added to the velocity attributes only. The velocities of
the object and goal are permanently set to 0. The object's x-position is selected uniformly
between [-0.3, 0] while the y-position is selected uniformly between [-0.2, 0.2], and this
process is repeated until the vector norm between the object's (x,y) position and origin is not greater
than 0.17. The goal always have the same position of (0.45, -0.05, -0.323).
The default framerate is 5 with each frame lasting for 0.01, giving rise to a *dt = 5 * 0.01 = 0.05*
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 100 timesteps.
2. Any of the state space values is no longer finite.
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('Pusher-v2')
```
There is no v3 for Pusher, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "pusher.xml", 5)
def step(self, a):
vec_1 = self.get_body_com("object") - self.get_body_com("tips_arm")
vec_2 = self.get_body_com("object") - self.get_body_com("goal")
reward_near = -np.linalg.norm(vec_1)
reward_dist = -np.linalg.norm(vec_2)
reward_ctrl = -np.square(a).sum()
reward = reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near
self.do_simulation(a, self.frame_skip)
ob = self._get_obs()
done = False
return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def viewer_setup(self):
self.viewer.cam.trackbodyid = -1
self.viewer.cam.distance = 4.0
def reset_model(self):
qpos = self.init_qpos
self.goal_pos = np.asarray([0, 0])
while True:
self.cylinder_pos = np.concatenate(
[
self.np_random.uniform(low=-0.3, high=0, size=1),
self.np_random.uniform(low=-0.2, high=0.2, size=1),
]
)
if np.linalg.norm(self.cylinder_pos - self.goal_pos) > 0.17:
break
qpos[-4:-2] = self.cylinder_pos
qpos[-2:] = self.goal_pos
qvel = self.init_qvel + self.np_random.uniform(
low=-0.005, high=0.005, size=self.model.nv
)
qvel[-4:] = 0
self.set_state(qpos, qvel)
return self._get_obs()
def _get_obs(self):
return np.concatenate(
[
self.data.qpos.flat[:7],
self.data.qvel.flat[:7],
self.get_body_com("tips_arm"),
self.get_body_com("object"),
self.get_body_com("goal"),
]
)

116
gym/envs/mujoco/reacher.py
View File

@@ -5,121 +5,11 @@ from gym.envs.mujoco import mujoco_env
class ReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
"Reacher" is a two-jointed robot arm. The goal is to move the robot's end effector (called *fingertip*) close to a
target that is spawned at a random position.
### Action Space
The action space is a `Box(-1, 1, (2,), float32)`. An action `(a, b)` represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------------------------------|-------------|-------------|--------------------------|-------|------|
| 0 | Torque applied at the first hinge (connecting the link to the point of fixture) | -1 | 1 | joint0 | hinge | torque (N m) |
| 1 | Torque applied at the second hinge (connecting the two links) | -1 | 1 | joint1 | hinge | torque (N m) |
### Observation Space
Observations consist of
- The cosine of the angles of the two arms
- The sine of the angles of the two arms
- The coordinates of the target
- The angular velocities of the arms
- The vector between the target and the reacher's fingertip (3 dimensional with the last element being 0)
The observation is a `ndarray` with shape `(11,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | cosine of the angle of the first arm | -Inf | Inf | cos(joint0) | hinge | unitless |
| 1 | cosine of the angle of the second arm | -Inf | Inf | cos(joint1) | hinge | unitless |
| 2 | sine of the angle of the first arm | -Inf | Inf | cos(joint0) | hinge | unitless |
| 3 | sine of the angle of the second arm | -Inf | Inf | cos(joint1) | hinge | unitless |
| 4 | x-coorddinate of the target | -Inf | Inf | target_x | slide | position (m) |
| 5 | y-coorddinate of the target | -Inf | Inf | target_y | slide | position (m) |
| 6 | angular velocity of the first arm | -Inf | Inf | joint0 | hinge | angular velocity (rad/s) |
| 7 | angular velocity of the second arm | -Inf | Inf | joint1 | hinge | angular velocity (rad/s) |
| 8 | x-value of position_fingertip - position_target | -Inf | Inf | NA | slide | position (m) |
| 9 | y-value of position_fingertip - position_target | -Inf | Inf | NA | slide | position (m) |
| 10 | z-value of position_fingertip - position_target (0 since reacher is 2d and z is same for both) | -Inf | Inf | NA | slide | position (m) |
Most Gym environments just return the positions and velocity of the
joints in the `.xml` file as the state of the environment. However, in
reacher the state is created by combining only certain elements of the
position and velocity, and performing some function transformations on them.
If one is to read the `.xml` for reacher then they will find 4 joints:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|-----------------------------|----------|----------|----------------------------------|-------|--------------------|
| 0 | angle of the first arm | -Inf | Inf | joint0 | hinge | angle (rad) |
| 1 | angle of the second arm | -Inf | Inf | joint1 | hinge | angle (rad) |
| 2 | x-coordinate of the target | -Inf | Inf | target_x | slide | position (m) |
| 3 | y-coordinate of the target | -Inf | Inf | target_y | slide | position (m) |
### Rewards
The reward consists of two parts:
- *reward_distance*: This reward is a measure of how far the *fingertip*
of the reacher (the unattached end) is from the target, with a more negative
value assigned for when the reacher's *fingertip* is further away from the
target. It is calculated as the negative vector norm of (position of
the fingertip - position of target), or *-norm("fingertip" - "target")*.
- *reward_control*: A negative reward for penalising the walker if
it takes actions that are too large. It is measured as the negative squared
Euclidean norm of the action, i.e. as *- sum(action<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *reward_distance + reward_control*
Unlike other environments, Reacher does not allow you to specify weights for the individual reward terms.
However, `info` does contain the keys *reward_dist* and *reward_ctrl*. Thus, if you'd like to weight the terms,
you should create a wrapper that computes the weighted reward from `info`.
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
with a noise added for stochasticity. A uniform noise in the range
[-0.1, 0.1] is added to the positional attributes, while the target position
is selected uniformly at random in a disk of radius 0.2 around the origin.
Independent, uniform noise in the
range of [-0.005, 0.005] is added to the velocities, and the last
element ("fingertip" - "target") is calculated at the end once everything
is set. The default setting has a framerate of 2 and a *dt = 2 * 0.01 = 0.02*
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 50 timesteps (with a new random target popping up if the reacher's fingertip reaches it before 50 timesteps)
2. Any of the state space values is no longer finite.
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('Reacher-v2')
```
There is no v3 for Reacher, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "reacher.xml", 2)
mujoco_env.MujocoEnv.__init__(
self, "reacher.xml", 2, mujoco_bindings="mujoco_py"
)
def step(self, a):
vec = self.get_body_com("fingertip") - self.get_body_com("target")

165
gym/envs/mujoco/reacher_v4.py Normal file
View File

@@ -0,0 +1,165 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class ReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
"Reacher" is a two-jointed robot arm. The goal is to move the robot's end effector (called *fingertip*) close to a
target that is spawned at a random position.
### Action Space
The action space is a `Box(-1, 1, (2,), float32)`. An action `(a, b)` represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|---------------------------------------------------------------------------------|-------------|-------------|--------------------------|-------|------|
| 0 | Torque applied at the first hinge (connecting the link to the point of fixture) | -1 | 1 | joint0 | hinge | torque (N m) |
| 1 | Torque applied at the second hinge (connecting the two links) | -1 | 1 | joint1 | hinge | torque (N m) |
### Observation Space
Observations consist of
- The cosine of the angles of the two arms
- The sine of the angles of the two arms
- The coordinates of the target
- The angular velocities of the arms
- The vector between the target and the reacher's fingertip (3 dimensional with the last element being 0)
The observation is a `ndarray` with shape `(11,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | cosine of the angle of the first arm | -Inf | Inf | cos(joint0) | hinge | unitless |
| 1 | cosine of the angle of the second arm | -Inf | Inf | cos(joint1) | hinge | unitless |
| 2 | sine of the angle of the first arm | -Inf | Inf | cos(joint0) | hinge | unitless |
| 3 | sine of the angle of the second arm | -Inf | Inf | cos(joint1) | hinge | unitless |
| 4 | x-coorddinate of the target | -Inf | Inf | target_x | slide | position (m) |
| 5 | y-coorddinate of the target | -Inf | Inf | target_y | slide | position (m) |
| 6 | angular velocity of the first arm | -Inf | Inf | joint0 | hinge | angular velocity (rad/s) |
| 7 | angular velocity of the second arm | -Inf | Inf | joint1 | hinge | angular velocity (rad/s) |
| 8 | x-value of position_fingertip - position_target | -Inf | Inf | NA | slide | position (m) |
| 9 | y-value of position_fingertip - position_target | -Inf | Inf | NA | slide | position (m) |
| 10 | z-value of position_fingertip - position_target (0 since reacher is 2d and z is same for both) | -Inf | Inf | NA | slide | position (m) |
Most Gym environments just return the positions and velocity of the
joints in the `.xml` file as the state of the environment. However, in
reacher the state is created by combining only certain elements of the
position and velocity, and performing some function transformations on them.
If one is to read the `.xml` for reacher then they will find 4 joints:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|-----------------------------|----------|----------|----------------------------------|-------|--------------------|
| 0 | angle of the first arm | -Inf | Inf | joint0 | hinge | angle (rad) |
| 1 | angle of the second arm | -Inf | Inf | joint1 | hinge | angle (rad) |
| 2 | x-coordinate of the target | -Inf | Inf | target_x | slide | position (m) |
| 3 | y-coordinate of the target | -Inf | Inf | target_y | slide | position (m) |
### Rewards
The reward consists of two parts:
- *reward_distance*: This reward is a measure of how far the *fingertip*
of the reacher (the unattached end) is from the target, with a more negative
value assigned for when the reacher's *fingertip* is further away from the
target. It is calculated as the negative vector norm of (position of
the fingertip - position of target), or *-norm("fingertip" - "target")*.
- *reward_control*: A negative reward for penalising the walker if
it takes actions that are too large. It is measured as the negative squared
Euclidean norm of the action, i.e. as *- sum(action<sup>2</sup>)*.
The total reward returned is ***reward*** *=* *reward_distance + reward_control*
Unlike other environments, Reacher does not allow you to specify weights for the individual reward terms.
However, `info` does contain the keys *reward_dist* and *reward_ctrl*. Thus, if you'd like to weight the terms,
you should create a wrapper that computes the weighted reward from `info`.
### Starting State
All observations start in state
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
with a noise added for stochasticity. A uniform noise in the range
[-0.1, 0.1] is added to the positional attributes, while the target position
is selected uniformly at random in a disk of radius 0.2 around the origin.
Independent, uniform noise in the
range of [-0.005, 0.005] is added to the velocities, and the last
element ("fingertip" - "target") is calculated at the end once everything
is set. The default setting has a framerate of 2 and a *dt = 2 * 0.01 = 0.02*
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 50 timesteps (with a new random target popping up if the reacher's fingertip reaches it before 50 timesteps)
2. Any of the state space values is no longer finite.
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('Reacher-v2')
```
There is no v3 for Reacher, unlike the robot environments where a v3 and
beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(self):
utils.EzPickle.__init__(self)
mujoco_env.MujocoEnv.__init__(self, "reacher.xml", 2)
def step(self, a):
vec = self.get_body_com("fingertip") - self.get_body_com("target")
reward_dist = -np.linalg.norm(vec)
reward_ctrl = -np.square(a).sum()
reward = reward_dist + reward_ctrl
self.do_simulation(a, self.frame_skip)
ob = self._get_obs()
done = False
return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def viewer_setup(self):
self.viewer.cam.trackbodyid = 0
def reset_model(self):
qpos = (
self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq)
+ self.init_qpos
)
while True:
self.goal = self.np_random.uniform(low=-0.2, high=0.2, size=2)
if np.linalg.norm(self.goal) < 0.2:
break
qpos[-2:] = self.goal
qvel = self.init_qvel + self.np_random.uniform(
low=-0.005, high=0.005, size=self.model.nv
)
qvel[-2:] = 0
self.set_state(qpos, qvel)
return self._get_obs()
def _get_obs(self):
theta = self.data.qpos.flat[:2]
return np.concatenate(
[
np.cos(theta),
np.sin(theta),
self.data.qpos.flat[2:],
self.data.qvel.flat[:2],
self.get_body_com("fingertip") - self.get_body_com("target"),
]
)

4
gym/envs/mujoco/swimmer.py
View File

@@ -6,7 +6,9 @@ from gym.envs.mujoco import mujoco_env
class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "swimmer.xml", 4)
mujoco_env.MujocoEnv.__init__(
self, "swimmer.xml", 4, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def step(self, a):

115
gym/envs/mujoco/swimmer_v3.py
View File

@@ -9,119 +9,6 @@ DEFAULT_CAMERA_CONFIG = {}
class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment corresponds to the Swimmer environment described in Rémi Coulom's PhD thesis
["Reinforcement Learning Using Neural Networks, with Applications to Motor Control"](https://tel.archives-ouvertes.fr/tel-00003985/document).
The environment aims to increase the number of independent state and control
variables as compared to the classic control environments. The swimmers
consist of three or more segments ('***links***') and one less articulation
joints ('***rotors***') - one rotor joint connecting exactly two links to
form a linear chain. The swimmer is suspended in a two dimensional pool and
always starts in the same position (subject to some deviation drawn from an
uniform distribution), and the goal is to move as fast as possible towards
the right by applying torque on the rotors and using the fluids friction.
### Notes
The problem parameters are:
Problem parameters:
* *n*: number of body parts
* *m<sub>i</sub>*: mass of part *i* (*i* ∈ {1...n})
* *l<sub>i</sub>*: length of part *i* (*i* ∈ {1...n})
* *k*: viscous-friction coefficient
While the default environment has *n* = 3, *l<sub>i</sub>* = 0.1,
and *k* = 0.1. It is possible to pass a custom MuJoCo XML file during construction to increase the
number of links, or to tweak any of the parameters.
### Action Space
The action space is a `Box(-1, 1, (2,), float32)`. An action represents the torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the first rotor | -1 | 1 | rot2 | hinge | torque (N m) |
| 1 | Torque applied on the second rotor | -1 | 1 | rot3 | hinge | torque (N m) |
### Observation Space
By default, observations consists of:
* θ<sub>i</sub>: angle of part *i* with respect to the *x* axis
* θ<sub>i</sub>': its derivative with respect to time (angular velocity)
In the default case, observations do not include the x- and y-coordinates of the front tip. These may
be included by passing `exclude_current_positions_from_observation=False` during construction.
Then, the observation space will have 10 dimensions where the first two dimensions
represent the x- and y-coordinates of the front tip.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
By default, the observation is a `ndarray` with shape `(8,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | angle of the front tip | -Inf | Inf | rot | hinge | angle (rad) |
| 1 | angle of the second rotor | -Inf | Inf | rot2 | hinge | angle (rad) |
| 2 | angle of the second rotor | -Inf | Inf | rot3 | hinge | angle (rad) |
| 3 | velocity of the tip along the x-axis | -Inf | Inf | slider1 | slide | velocity (m/s) |
| 4 | velocity of the tip along the y-axis | -Inf | Inf | slider2 | slide | velocity (m/s) |
| 5 | angular velocity of front tip | -Inf | Inf | rot | hinge | angular velocity (rad/s) |
| 6 | angular velocity of second rotor | -Inf | Inf | rot2 | hinge | angular velocity (rad/s) |
| 7 | angular velocity of third rotor | -Inf | Inf | rot3 | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of two parts:
- *forward_reward*: A reward of moving forward which is measured
as *`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependent on the frame_skip parameter
(default is 4), where the frametime is 0.01 - making the
default *dt = 4 * 0.01 = 0.04*. This reward would be positive if the swimmer
swims right as desired.
- *ctrl_cost*: A cost for penalising the swimmer if it takes
actions that are too large. It is measured as *`ctrl_cost_weight` *
sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is a parameter set for the
control and has a default value of 1e-4
The total reward returned is ***reward*** *=* *forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state (0,0,0,0,0,0,0,0) with a Uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] is added to the initial state for stochasticity.
### Episode Termination
The episode terminates when the episode length is greater than 1000.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
gym.make('Swimmer-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Swimmer-v3', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|--------------|-------------------------------|
| `xml_file` | **str** | `"swimmer.xml"`| Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.0` | Weight for *forward_reward* term (see section on reward) |
| `ctrl_cost_weight` | **float** | `1e-4` | Weight for *ctrl_cost* term (see section on reward) |
| `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True`| Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="swimmer.xml",
@@ -141,7 +28,7 @@ class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4, mujoco_bindings="mujoco_py")
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))

217
gym/envs/mujoco/swimmer_v4.py Normal file
View File

@@ -0,0 +1,217 @@
__credits__ = ["Rushiv Arora"]
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {}
class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment corresponds to the Swimmer environment described in Rémi Coulom's PhD thesis
["Reinforcement Learning Using Neural Networks, with Applications to Motor Control"](https://tel.archives-ouvertes.fr/tel-00003985/document).
The environment aims to increase the number of independent state and control
variables as compared to the classic control environments. The swimmers
consist of three or more segments ('***links***') and one less articulation
joints ('***rotors***') - one rotor joint connecting exactly two links to
form a linear chain. The swimmer is suspended in a two dimensional pool and
always starts in the same position (subject to some deviation drawn from an
uniform distribution), and the goal is to move as fast as possible towards
the right by applying torque on the rotors and using the fluids friction.
### Notes
The problem parameters are:
Problem parameters:
* *n*: number of body parts
* *m<sub>i</sub>*: mass of part *i* (*i* ∈ {1...n})
* *l<sub>i</sub>*: length of part *i* (*i* ∈ {1...n})
* *k*: viscous-friction coefficient
While the default environment has *n* = 3, *l<sub>i</sub>* = 0.1,
and *k* = 0.1. It is possible to tweak the MuJoCo XML files to increase the
number of links, or to tweak any of the parameters.
### Action Space
The agent take a 2-element vector for actions.
The action space is a continuous `(action, action)` in `[-1, 1]`, where
`action` represents the numerical torques applied between *links*
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the first rotor | -1 | 1 | rot2 | hinge | torque (N m) |
| 1 | Torque applied on the second rotor | -1 | 1 | rot3 | hinge | torque (N m) |
### Observation Space
The state space consists of:
* A<sub>0</sub>: position of first point
* θ<sub>i</sub>: angle of part *i* with respect to the *x* axis
* A<sub>0</sub>, θ<sub>i</sub>: their derivatives with respect to time (velocity and angular velocity)
The observation is a `ndarray` with shape `(8,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | x-coordinate of the front tip | -Inf | Inf | slider1 | slide | position (m) |
| 1 | y-coordinate of the front tip | -Inf | Inf | slider2 | slide | position (m) |
| 2 | angle of the front tip | -Inf | Inf | rot | hinge | angle (rad) |
| 3 | angle of the second rotor | -Inf | Inf | rot2 | hinge | angle (rad) |
| 4 | angle of the second rotor | -Inf | Inf | rot3 | hinge | angle (rad) |
| 5 | velocity of the tip along the x-axis | -Inf | Inf | slider1 | slide | velocity (m/s) |
| 6 | velocity of the tip along the y-axis | -Inf | Inf | slider2 | slide | velocity (m/s) |
| 7 | angular velocity of front tip | -Inf | Inf | rot | hinge | angular velocity (rad/s) |
| 8 | angular velocity of second rotor | -Inf | Inf | rot2 | hinge | angular velocity (rad/s) |
| 9 | angular velocity of third rotor | -Inf | Inf | rot3 | hinge | angular velocity (rad/s) |
**Note:**
In practice (and Gym implementation), the first two positional elements are
omitted from the state space since the reward function is calculated based
on those values. Therefore, observation space has shape `(8,)` and looks like:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint| Unit |
|-----|-----------------------|----------------------|--------------------|----------------------|--------------------|--------------------|
| 0 | angle of the front tip | -Inf | Inf | rot | hinge | angle (rad) |
| 1 | angle of the second rotor | -Inf | Inf | rot2 | hinge | angle (rad) |
| 2 | angle of the second rotor | -Inf | Inf | rot3 | hinge | angle (rad) |
| 3 | velocity of the tip along the x-axis | -Inf | Inf | slider1 | slide | velocity (m/s) |
| 4 | velocity of the tip along the y-axis | -Inf | Inf | slider2 | slide | velocity (m/s) |
| 5 | angular velocity of front tip | -Inf | Inf | rot | hinge | angular velocity (rad/s) |
| 6 | angular velocity of second rotor | -Inf | Inf | rot2 | hinge | angular velocity (rad/s) |
| 7 | angular velocity of third rotor | -Inf | Inf | rot3 | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of two parts:
- *reward_front*: A reward of moving forward which is measured
as *(x-coordinate before action - x-coordinate after action)/dt*. *dt* is
the time between actions and is dependeent on the frame_skip parameter
(default is 4), where the *dt* for one frame is 0.01 - making the
default *dt = 4 * 0.01 = 0.04*. This reward would be positive if the swimmer
swims right as desired.
- *reward_control*: A negative reward for penalising the swimmer if it takes
actions that are too large. It is measured as *-coefficient x
sum(action<sup>2</sup>)* where *coefficient* is a parameter set for the
control and has a default value of 0.0001
The total reward returned is ***reward*** *=* *reward_front + reward_control*
### Starting State
All observations start in state (0,0,0,0,0,0,0,0) with a Uniform noise in the range of [-0.1, 0.1] is added to the initial state for stochasticity.
### Episode Termination
The episode terminates when the episode length is greater than 1000.
### Arguments
No additional arguments are currently supported (in v2 and lower), but
modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder).
```
gym.make('Swimmer-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Swimmer-v3', ctrl_cost_weight=0.1, ....)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="swimmer.xml",
forward_reward_weight=1.0,
ctrl_cost_weight=1e-4,
reset_noise_scale=0.1,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
def step(self, action):
xy_position_before = self.data.qpos[0:2].copy()
self.do_simulation(action, self.frame_skip)
xy_position_after = self.data.qpos[0:2].copy()
xy_velocity = (xy_position_after - xy_position_before) / self.dt
x_velocity, y_velocity = xy_velocity
forward_reward = self._forward_reward_weight * x_velocity
ctrl_cost = self.control_cost(action)
observation = self._get_obs()
reward = forward_reward - ctrl_cost
done = False
info = {
"reward_fwd": forward_reward,
"reward_ctrl": -ctrl_cost,
"x_position": xy_position_after[0],
"y_position": xy_position_after[1],
"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
"x_velocity": x_velocity,
"y_velocity": y_velocity,
"forward_reward": forward_reward,
}
return observation, reward, done, info
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = self.data.qvel.flat.copy()
if self._exclude_current_positions_from_observation:
position = position[2:]
observation = np.concatenate([position, velocity]).ravel()
return observation
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nv
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

4
gym/envs/mujoco/walker2d.py
View File

@@ -6,7 +6,9 @@ from gym.envs.mujoco import mujoco_env
class Walker2dEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, "walker2d.xml", 4)
mujoco_env.MujocoEnv.__init__(
self, "walker2d.xml", 4, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
def step(self, a):

133
gym/envs/mujoco/walker2d_v3.py
View File

@@ -12,137 +12,6 @@ DEFAULT_CAMERA_CONFIG = {
class Walker2dEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment builds on the hopper environment based on the work done by Erez, Tassa, and Todorov
in ["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf)
by adding another set of legs making it possible for the robot to walker forward instead of
hop. Like other Mujoco environments, this environment aims to increase the number of independent state
and control variables as compared to the classic control environments. The walker is a
two-dimensional two-legged figure that consist of four main body parts - a single torso at the top
(with the two legs splitting after the torso), two thighs in the middle below the torso, two legs
in the bottom below the thighs, and two feet attached to the legs on which the entire body rests.
The goal is to make coordinate both sets of feet, legs, and thighs to move in the forward (right)
direction by applying torques on the six hinges connecting the six body parts.
### Action Space
The action space is a `Box(-1, 1, (6,), float32)`. An action represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
| 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
| 3 | Torque applied on the left thigh rotor | -1 | 1 | thigh_left_joint | hinge | torque (N m) |
| 4 | Torque applied on the left leg rotor | -1 | 1 | leg_left_joint | hinge | torque (N m) |
| 5 | Torque applied on the left foot rotor | -1 | 1 | foot_left_joint | hinge | torque (N m) |
### Observation Space
Observations consist of positional values of different body parts of the walker,
followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
By default, observations do not include the x-coordinate of the top. It may
be included by passing `exclude_current_positions_from_observation=False` during construction.
In that case, the observation space will have 18 dimensions where the first dimension
represent the x-coordinates of the top of the walker.
Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate
of the top will be returned in `info` with key `"x_position"`.
By default, observation is a `ndarray` with shape `(17,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|--------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) |
| 1 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) |
| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 5 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) |
| 6 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) |
| 7 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) |
| 8 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 9 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 10 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 11 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 12 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 13 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
| 14 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) |
| 15 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) |
| 16 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of three parts:
- *healthy_reward*: Every timestep that the walker is alive, it receives a fixed reward of value `healthy_reward`,
- *forward_reward*: A reward of walking forward which is measured as
*`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*.
*dt* is the time between actions and is dependeent on the frame_skip parameter
(default is 4), where the frametime is 0.002 - making the default
*dt = 4 * 0.002 = 0.008*. This reward would be positive if the walker walks forward (right) desired.
- *ctrl_cost*: A cost for penalising the walker if it
takes actions that are too large. It is measured as
*`ctrl_cost_weight` * sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is
a parameter set for the control and has a default value of 0.001
The total reward returned is ***reward*** *=* *healthy_reward bonus + forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
### Starting State
All observations start in state
(0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
with a uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] added to the values for stochasticity.
### Episode Termination
The walker is said to be unhealthy if any of the following happens:
1. Any of the state space values is no longer finite
2. The height of the walker is ***not*** in the closed interval specified by `healthy_z_range`
3. The absolute value of the angle (`observation[1]` if `exclude_current_positions_from_observation=False`, else `observation[2]`) is ***not*** in the closed interval specified by `healthy_angle_range`
If `terminate_when_unhealthy=True` is passed during construction (which is the default),
the episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. The walker is unhealthy
If `terminate_when_unhealthy=False` is passed, the episode is terminated only when 1000 timesteps are exceeded.
### Arguments
No additional arguments are currently supported in v2 and lower.
```
env = gym.make('Walker2d-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Walker2d-v3', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default |Description |
|-------------------------|------------|--------------|-------------------------------|
| `xml_file` | **str** | `"walker2d.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.0` | Weight for *forward_reward* term (see section on reward) |
| `ctrl_cost_weight` | **float** | `1e-3` | Weight for *ctr_cost* term (see section on reward) |
| `healthy_reward` | **float** | `1.0` | Constant reward given if the ant is "healthy" after timestep |
| `terminate_when_unhealthy` | **bool**| `True` | If true, issue a done signal if the z-coordinate of the walker is no longer healthy |
| `healthy_z_range` | **tuple** | `(0.8, 2)` | The z-coordinate of the top of the walker must be in this range to be considered healthy |
| `healthy_angle_range` | **tuple** | `(-1, 1)` | The angle must be in this range to be considered healthy|
| `reset_noise_scale` | **float** | `5e-3` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
| `exclude_current_positions_from_observation`| **bool** | `True`| Whether or not to omit the x-coordinate from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
### Version History
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="walker2d.xml",
@@ -172,7 +41,7 @@ class Walker2dEnv(mujoco_env.MujocoEnv, utils.EzPickle):
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4, mujoco_bindings="mujoco_py")
@property
def healthy_reward(self):

276
gym/envs/mujoco/walker2d_v4.py Normal file
View File

@@ -0,0 +1,276 @@
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 2,
"distance": 4.0,
"lookat": np.array((0.0, 0.0, 1.15)),
"elevation": -20.0,
}
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 2,
"distance": 4.0,
"lookat": np.array((0.0, 0.0, 1.15)),
"elevation": -20.0,
}
class Walker2dEnv(mujoco_env.MujocoEnv, utils.EzPickle):
"""
### Description
This environment builds on the hopper environment based on the work done by Erez, Tassa, and Todorov
in ["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf)
by adding another set of legs making it possiible for the robot to walker forward instead of
hop. Like other Mujoco environments, this environment aims to increase the number of independent state
and control variables as compared to the classic control environments. The walker is a
two-dimensional two-legged figure that consist of four main body parts - a single torso at the top
(with the two legs splitting after the torso), two thighs in the middle below the torso, two legs
in the bottom below the thighs, and two feet attached to the legs on which the entire body rests.
The goal is to make coordinate both sets of feet, legs, and thighs to move in the forward (right)
direction by applying torques on the six hinges connecting the six body parts.
### Action Space
The agent take a 6-element vector for actions.
The action space is a continuous `(action, action, action, action, action, action)`
all in `[-1, 1]`, where `action` represents the numerical torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
|-----|----------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
| 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
| 3 | Torque applied on the left thigh rotor | -1 | 1 | thigh_left_joint | hinge | torque (N m) |
| 4 | Torque applied on the left leg rotor | -1 | 1 | leg_left_joint | hinge | torque (N m) |
| 5 | Torque applied on the left foot rotor | -1 | 1 | foot_left_joint | hinge | torque (N m) |
### Observation Space
The state space consists of positional values of different body parts of the walker,
followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
The observation is a `ndarray` with shape `(17,)` where the elements correspond to the following:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|--------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | x-coordinate of the top | -Inf | Inf | rootx (torso) | slide | position (m) |
| 1 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) |
| 2 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) |
| 3 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 4 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 5 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 6 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) |
| 7 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) |
| 8 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) |
| 9 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 10 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 11 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 12 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 13 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 14 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
| 15 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) |
| 16 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) |
| 17 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) |
**Note:**
In practice (and Gym implementation), the first positional element is omitted from the
state space since the reward function is calculated based on that value. This value is
hidden from the algorithm, which in turn has to develop an abstract understanding of it
from the observed rewards. Therefore, observation space has shape `(17,)`
instead of `(18,)` and looks like:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
|-----|--------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) |
| 1 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) |
| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
| 5 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) |
| 6 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) |
| 7 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) |
| 8 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
| 9 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
| 10 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
| 11 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
| 12 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
| 13 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
| 14 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) |
| 15 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) |
| 16 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) |
### Rewards
The reward consists of three parts:
- *alive bonus*: Every timestep that the walker is alive, it gets a reward of 1,
- *reward_forward*: A reward of walking forward which is measured as
*(x-coordinate before action - x-coordinate after action)/dt*.
*dt* is the time between actions and is dependeent on the frame_skip parameter
(default is 4), where the *dt* for one frame is 0.002 - making the default
*dt = 4 * 0.002 = 0.008*. This reward would be positive if the walker walks forward (right) desired.
- *reward_control*: A negative reward for penalising the walker if it
takes actions that are too large. It is measured as
*-coefficient **x** sum(action<sup>2</sup>)* where *coefficient* is
a parameter set for the control and has a default value of 0.001
The total reward returned is ***reward*** *=* *alive bonus + reward_forward + reward_control*
### Starting State
All observations start in state
(0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
with a uniform noise in the range of [-0.005, 0.005] added to the values for stochasticity.
### Episode Termination
The episode terminates when any of the following happens:
1. The episode duration reaches a 1000 timesteps
2. Any of the state space values is no longer finite
3. The height of the walker (index 1) is ***not*** in the range `[0.8, 2]`
4. The absolute value of the angle (index 2) is ***not*** in the range `[-1, 1]`
### Arguments
No additional arguments are currently supported (in v2 and lower),
but modifications can be made to the XML file in the assets folder
(or by changing the path to a modified XML file in another folder)..
```
env = gym.make('Walker2d-v2')
```
v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
```
env = gym.make('Walker2d-v3', ctrl_cost_weight=0.1, ....)
```
### Version History
* v4: all mujoco environments now use the mujoco binidings in mujoco>=2.1.3
* v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
* v2: All continuous control environments now use mujoco_py >= 1.50
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release (1.0.0)
"""
def __init__(
self,
xml_file="walker2d.xml",
forward_reward_weight=1.0,
ctrl_cost_weight=1e-3,
healthy_reward=1.0,
terminate_when_unhealthy=True,
healthy_z_range=(0.8, 2.0),
healthy_angle_range=(-1.0, 1.0),
reset_noise_scale=5e-3,
exclude_current_positions_from_observation=True,
):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_z_range = healthy_z_range
self._healthy_angle_range = healthy_angle_range
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
@property
def healthy_reward(self):
return (
float(self.is_healthy or self._terminate_when_unhealthy)
* self._healthy_reward
)
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
@property
def is_healthy(self):
z, angle = self.data.qpos[1:3]
min_z, max_z = self._healthy_z_range
min_angle, max_angle = self._healthy_angle_range
healthy_z = min_z < z < max_z
healthy_angle = min_angle < angle < max_angle
is_healthy = healthy_z and healthy_angle
return is_healthy
@property
def done(self):
done = not self.is_healthy if self._terminate_when_unhealthy else False
return done
def _get_obs(self):
position = self.data.qpos.flat.copy()
velocity = np.clip(self.data.qvel.flat.copy(), -10, 10)
if self._exclude_current_positions_from_observation:
position = position[1:]
observation = np.concatenate((position, velocity)).ravel()
return observation
def step(self, action):
x_position_before = self.data.qpos[0]
self.do_simulation(action, self.frame_skip)
x_position_after = self.data.qpos[0]
x_velocity = (x_position_after - x_position_before) / self.dt
ctrl_cost = self.control_cost(action)
forward_reward = self._forward_reward_weight * x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
costs = ctrl_cost
observation = self._get_obs()
reward = rewards - costs
done = self.done
info = {
"x_position": x_position_after,
"x_velocity": x_velocity,
}
return observation, reward, done, info
def reset_model(self):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.uniform(
low=noise_low, high=noise_high, size=self.model.nv
)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)

9
py.Dockerfile
View File

@@ -6,15 +6,14 @@ RUN apt-get -y update && apt-get install -y unzip libglu1-mesa-dev libgl1-mesa-d
# Download mujoco
RUN mkdir /root/.mujoco && \
cd /root/.mujoco && \
curl -O https://www.roboti.us/download/mjpro150_linux.zip && \
unzip mjpro150_linux.zip && \
echo DUMMY_KEY > /root/.mujoco/mjkey.txt
wget https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz &&\
tar -xf mujoco210-linux-x86_64.tar.gz
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mjpro150/bin
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mujoco210/bin
COPY . /usr/local/gym/
WORKDIR /usr/local/gym/
RUN pip install .[nomujoco] && pip install -r test_requirements.txt
RUN pip install .[noatari] && pip install -r test_requirements.txt
ENTRYPOINT ["/usr/local/gym/bin/docker_entrypoint"]

1
requirements.txt
View File

@@ -1,7 +1,6 @@
ale-py~=0.7
opencv-python>=3.0
box2d-py==2.3.5
mujoco_py>=1.50, <2.0
scipy>=1.4.1
numpy>=1.18.0
pyglet>=1.4.0

10
setup.py
View File

@@ -15,19 +15,25 @@ extras = {
"accept-rom-license": ["autorom[accept-rom-license]~=0.4.2"],
"box2d": ["box2d-py==2.3.5", "pygame==2.1.0"],
"classic_control": ["pygame==2.1.0"],
"mujoco": ["mujoco_py>=1.50, <2.0"],
"mujoco_py": ["mujoco_py<2.2,>=2.1"],
"mujoco": ["mujoco==2.1.5", "imageio>=2.14.1"],
"toy_text": ["pygame==2.1.0", "scipy>=1.4.1"],
"other": ["lz4>=3.1.0", "opencv-python>=3.0", "matplotlib>=3.0"],
}
# Meta dependency groups.
nomujoco_blacklist = {"mujoco", "accept-rom-license", "atari"}
nomujoco_blacklist = {"mujoco_py", "mujoco", "accept-rom-license", "atari"}
nomujoco_groups = set(extras.keys()) - nomujoco_blacklist
extras["nomujoco"] = list(
itertools.chain.from_iterable(map(lambda group: extras[group], nomujoco_groups))
)
noatari_blacklist = {"accept-rom-license", "atari"}
noatari_groups = set(extras.keys()) - noatari_blacklist
extras["noatari"] = list(
itertools.chain.from_iterable(map(lambda group: extras[group], noatari_groups))
)
all_blacklist = {"accept-rom-license"}
all_groups = set(extras.keys()) - all_blacklist

26
tests/envs/spec_list.py
View File

@@ -1,19 +1,14 @@
import os
from gym import envs, logger
SKIP_MUJOCO_WARNING_MESSAGE = (
"Cannot run mujoco test (either license key not found or mujoco not"
"installed properly)."
SKIP_MUJOCO_V3_WARNING_MESSAGE = (
"Cannot run mujoco test because mujoco-py is not installed"
)
skip_mujoco = not (os.environ.get("MUJOCO_KEY"))
if not skip_mujoco:
skip_mujoco_v3 = False
try:
import mujoco_py # noqa:F401
except ImportError:
skip_mujoco = True
skip_mujoco_v3 = True
def should_skip_env_spec_for_tests(spec):
@@ -21,7 +16,7 @@ def should_skip_env_spec_for_tests(spec):
# troublesome to run frequently
ep = spec.entry_point
# Skip mujoco tests for pull request CI
if skip_mujoco and ep.startswith("gym.envs.mujoco"):
if skip_mujoco_v3 and ep.startswith("gym.envs.mujoco"):
return True
try:
import gym.envs.atari # noqa:F401
@@ -48,8 +43,19 @@ def should_skip_env_spec_for_tests(spec):
return False
def skip_mujoco_py_env_for_test(spec):
ep = spec.entry_point
version = spec.version
if ep.startswith("gym.envs.mujoco") and version < 4:
return True
return False
spec_list = [
spec
for spec in sorted(envs.registry.values(), key=lambda x: x.id)
if spec.entry_point is not None and not should_skip_env_spec_for_tests(spec)
]
spec_list_no_mujoco_py = [
spec for spec in spec_list if not skip_mujoco_py_env_for_test(spec)
]

14
tests/envs/test_action_dim_check.py
View File

@@ -8,17 +8,19 @@ from gym.envs.registration import EnvSpec
from gym.spaces.box import Box
from gym.spaces.discrete import Discrete
from gym.spaces.space import Space
from tests.envs.spec_list import SKIP_MUJOCO_WARNING_MESSAGE, skip_mujoco, spec_list
from tests.envs.spec_list import (
SKIP_MUJOCO_V3_WARNING_MESSAGE,
skip_mujoco_v3,
spec_list,
)
ENVIRONMENT_IDS = ("HalfCheetah-v2",)
def make_envs_by_action_space_type(spec_list: List[EnvSpec], action_space: Space):
"""Make environments of specific action_space type.
This function returns a filtered list of environment from the
spec_list that matches the action_space type.
Args:
spec_list (list): list of registered environments' specification
action_space (gym.spaces.Space): action_space type
@@ -31,7 +33,7 @@ def make_envs_by_action_space_type(spec_list: List[EnvSpec], action_space: Space
return filtered_envs
@pytest.mark.skipif(skip_mujoco, reason=SKIP_MUJOCO_WARNING_MESSAGE)
@pytest.mark.skipif(skip_mujoco_v3, reason=SKIP_MUJOCO_V3_WARNING_MESSAGE)
@pytest.mark.parametrize("environment_id", ENVIRONMENT_IDS)
def test_serialize_deserialize(environment_id):
env = envs.make(environment_id)
@@ -47,10 +49,8 @@ def test_serialize_deserialize(environment_id):
@pytest.mark.parametrize("env", make_envs_by_action_space_type(spec_list, Discrete))
def test_discrete_actions_out_of_bound(env):
"""Test out of bound actions in Discrete action_space.
In discrete action_space environments, `out-of-bound`
actions are not allowed and should raise an exception.
Args:
env (gym.Env): the gym environment
"""
@@ -69,11 +69,9 @@ def test_discrete_actions_out_of_bound(env):
)
def test_box_actions_out_of_bound(env, seed):
"""Test out of bound actions in Box action_space.
Environments with Box actions spaces perform clipping inside `step`.
The expected behaviour is that an action `out-of-bound` has the same effect
of an action with value exactly at the upper (or lower) bound.
Args:
env (gym.Env): the gym environment
seed (int): seed value for determinism

10
tests/envs/test_envs.py
View File

@@ -4,7 +4,7 @@ import pytest
from gym import envs
from gym.spaces import Box
from gym.utils.env_checker import check_env
from tests.envs.spec_list import spec_list
from tests.envs.spec_list import spec_list, spec_list_no_mujoco_py
# This runs a smoketest on each official registered env. We may want
@@ -13,7 +13,9 @@ from tests.envs.spec_list import spec_list
@pytest.mark.filterwarnings(
"ignore:.*We recommend you to use a symmetric and normalized Box action space.*"
)
@pytest.mark.parametrize("spec", spec_list, ids=[spec.id for spec in spec_list])
@pytest.mark.parametrize(
"spec", spec_list_no_mujoco_py, ids=[spec.id for spec in spec_list_no_mujoco_py]
)
def test_env(spec):
# Capture warnings
with pytest.warns(None) as warnings:
@@ -47,12 +49,14 @@ def test_env(spec):
assert (
observation.dtype == ob_space.dtype
), f"Step observation dtype: {ob.dtype}, expected: {ob_space.dtype}"
for mode in env.metadata.get("render_modes", []):
if not (mode == "human" and spec.entry_point.startswith("gym.envs.mujoco")):
env.render(mode=mode)
# Make sure we can render the environment after close.
for mode in env.metadata.get("render_modes", []):
if not (mode == "human" and spec.entry_point.startswith("gym.envs.mujoco")):
env.render(mode=mode)
env.close()

4
tests/envs/test_mujoco_v2_to_v3_conversion.py
View File

@@ -3,7 +3,7 @@ import unittest
import numpy as np
from gym import envs
from tests.envs.spec_list import SKIP_MUJOCO_WARNING_MESSAGE, skip_mujoco
from tests.envs.spec_list import SKIP_MUJOCO_V3_WARNING_MESSAGE, skip_mujoco_v3
def verify_environments_match(
@@ -31,7 +31,7 @@ def verify_environments_match(
np.testing.assert_allclose(old_info[key], new_info[key], atol=eps)
@unittest.skipIf(skip_mujoco, SKIP_MUJOCO_WARNING_MESSAGE)
@unittest.skipIf(skip_mujoco_v3, SKIP_MUJOCO_V3_WARNING_MESSAGE)
class Mujocov2Tov3ConversionTest(unittest.TestCase):
def test_environments_match(self):
test_cases = (