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262 lines
14 KiB
Python
262 lines
14 KiB
Python
import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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DEFAULT_CAMERA_CONFIG = {
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"trackbodyid": 2,
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"distance": 4.0,
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"lookat": np.array((0.0, 0.0, 1.15)),
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"elevation": -20.0,
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}
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class Walker2dEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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"""
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### Description
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This environment builds on the hopper environment based on the work done by Erez, Tassa, and Todorov
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in ["Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks"](http://www.roboticsproceedings.org/rss07/p10.pdf)
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by adding another set of legs making it possible for the robot to walker forward instead of
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hop. Like other Mujoco environments, this environment aims to increase the number of independent state
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and control variables as compared to the classic control environments. The walker is a
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two-dimensional two-legged figure that consist of four main body parts - a single torso at the top
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(with the two legs splitting after the torso), two thighs in the middle below the torso, two legs
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in the bottom below the thighs, and two feet attached to the legs on which the entire body rests.
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The goal is to make coordinate both sets of feet, legs, and thighs to move in the forward (right)
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direction by applying torques on the six hinges connecting the six body parts.
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### Action Space
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The action space is a `Box(-1, 1, (6,), float32)`. An action represents the torques applied at the hinge joints.
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| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|----------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
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| 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) |
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| 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) |
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| 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) |
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| 3 | Torque applied on the left thigh rotor | -1 | 1 | thigh_left_joint | hinge | torque (N m) |
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| 4 | Torque applied on the left leg rotor | -1 | 1 | leg_left_joint | hinge | torque (N m) |
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| 5 | Torque applied on the left foot rotor | -1 | 1 | foot_left_joint | hinge | torque (N m) |
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### Observation Space
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Observations consist of positional values of different body parts of the walker,
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followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities.
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By default, observations do not include the x-coordinate of the top. It may
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be included by passing `exclude_current_positions_from_observation=False` during construction.
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In that case, the observation space will have 18 dimensions where the first dimension
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represent the x-coordinates of the top of the walker.
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Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x-coordinate
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of the top will be returned in `info` with key `"x_position"`.
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By default, observation is a `ndarray` with shape `(17,)` where the elements correspond to the following:
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| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|--------------------------------------------------------|----------------|-----------------|----------------------------------------|-------|------|
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| 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) |
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| 1 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) |
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| 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) |
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| 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) |
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| 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) |
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| 5 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) |
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| 6 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) |
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| 7 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) |
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| 8 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) |
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| 9 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) |
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| 10 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
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| 11 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) |
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| 12 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) |
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| 13 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) |
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| 14 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) |
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| 15 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) |
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| 16 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) |
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### Rewards
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The reward consists of three parts:
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- *healthy_reward*: Every timestep that the walker is alive, it receives a fixed reward of value `healthy_reward`,
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- *forward_reward*: A reward of walking forward which is measured as
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*`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*.
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*dt* is the time between actions and is dependeent on the frame_skip parameter
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(default is 4), where the frametime is 0.002 - making the default
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*dt = 4 * 0.002 = 0.008*. This reward would be positive if the walker walks forward (right) desired.
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- *ctrl_cost*: A cost for penalising the walker if it
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takes actions that are too large. It is measured as
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*`ctrl_cost_weight` * sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is
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a parameter set for the control and has a default value of 0.001
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The total reward returned is ***reward*** *=* *healthy_reward bonus + forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
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### Starting State
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All observations start in state
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(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)
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with a uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] added to the values for stochasticity.
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### Episode Termination
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The walker is said to be unhealthy if any of the following happens:
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1. Any of the state space values is no longer finite
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2. The height of the walker is ***not*** in the closed interval specified by `healthy_z_range`
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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`
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If `terminate_when_unhealthy=True` is passed during construction (which is the default),
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the episode terminates when any of the following happens:
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1. The episode duration reaches a 1000 timesteps
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2. The walker is unhealthy
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If `terminate_when_unhealthy=False` is passed, the episode is terminated only when 1000 timesteps are exceeded.
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### Arguments
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No additional arguments are currently supported in v2 and lower.
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```
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env = gym.make('Walker2d-v2')
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```
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v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
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```
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env = gym.make('Walker2d-v3', ctrl_cost_weight=0.1, ....)
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```
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| Parameter | Type | Default |Description |
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|-------------------------|------------|--------------|-------------------------------|
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| `xml_file` | **str** | `"walker2d.xml"` | Path to a MuJoCo model |
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| `forward_reward_weight` | **float** | `1.0` | Weight for *forward_reward* term (see section on reward) |
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| `ctrl_cost_weight` | **float** | `1e-3` | Weight for *ctr_cost* term (see section on reward) |
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| `healthy_reward` | **float** | `1.0` | Constant reward given if the ant is "healthy" after timestep |
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| `terminate_when_unhealthy` | **bool**| `True` | If true, issue a done signal if the z-coordinate of the walker is no longer healthy |
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| `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 |
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| `healthy_angle_range` | **tuple** | `(-1, 1)` | The angle must be in this range to be considered healthy|
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| `reset_noise_scale` | **float** | `5e-3` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
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| `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 |
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### Version History
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* 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)
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* v2: All continuous control environments now use mujoco_py >= 1.50
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* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
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* v0: Initial versions release (1.0.0)
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"""
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def __init__(
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self,
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xml_file="walker2d.xml",
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forward_reward_weight=1.0,
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ctrl_cost_weight=1e-3,
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healthy_reward=1.0,
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terminate_when_unhealthy=True,
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healthy_z_range=(0.8, 2.0),
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healthy_angle_range=(-1.0, 1.0),
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reset_noise_scale=5e-3,
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exclude_current_positions_from_observation=True,
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):
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utils.EzPickle.__init__(**locals())
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self._forward_reward_weight = forward_reward_weight
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self._ctrl_cost_weight = ctrl_cost_weight
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self._healthy_reward = healthy_reward
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self._terminate_when_unhealthy = terminate_when_unhealthy
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self._healthy_z_range = healthy_z_range
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self._healthy_angle_range = healthy_angle_range
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self._reset_noise_scale = reset_noise_scale
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self._exclude_current_positions_from_observation = (
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exclude_current_positions_from_observation
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)
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mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
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@property
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def healthy_reward(self):
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return (
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float(self.is_healthy or self._terminate_when_unhealthy)
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* self._healthy_reward
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)
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def control_cost(self, action):
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control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
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return control_cost
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@property
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def is_healthy(self):
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z, angle = self.sim.data.qpos[1:3]
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min_z, max_z = self._healthy_z_range
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min_angle, max_angle = self._healthy_angle_range
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healthy_z = min_z < z < max_z
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healthy_angle = min_angle < angle < max_angle
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is_healthy = healthy_z and healthy_angle
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return is_healthy
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@property
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def done(self):
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done = not self.is_healthy if self._terminate_when_unhealthy else False
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return done
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def _get_obs(self):
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position = self.sim.data.qpos.flat.copy()
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velocity = np.clip(self.sim.data.qvel.flat.copy(), -10, 10)
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if self._exclude_current_positions_from_observation:
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position = position[1:]
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observation = np.concatenate((position, velocity)).ravel()
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return observation
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def step(self, action):
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x_position_before = self.sim.data.qpos[0]
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self.do_simulation(action, self.frame_skip)
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x_position_after = self.sim.data.qpos[0]
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x_velocity = (x_position_after - x_position_before) / self.dt
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ctrl_cost = self.control_cost(action)
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forward_reward = self._forward_reward_weight * x_velocity
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healthy_reward = self.healthy_reward
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rewards = forward_reward + healthy_reward
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costs = ctrl_cost
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observation = self._get_obs()
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reward = rewards - costs
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done = self.done
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info = {
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"x_position": x_position_after,
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"x_velocity": x_velocity,
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}
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return observation, reward, done, info
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def reset_model(self):
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noise_low = -self._reset_noise_scale
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noise_high = self._reset_noise_scale
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qpos = self.init_qpos + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nq
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)
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qvel = self.init_qvel + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nv
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)
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self.set_state(qpos, qvel)
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observation = self._get_obs()
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return observation
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def viewer_setup(self):
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for key, value in DEFAULT_CAMERA_CONFIG.items():
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if isinstance(value, np.ndarray):
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getattr(self.viewer.cam, key)[:] = value
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else:
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setattr(self.viewer.cam, key, value)
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