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283 lines
16 KiB
Python
283 lines
16 KiB
Python
__credits__ = ["Kallinteris-Andreas", "Rushiv Arora"]
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import numpy as np
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from gymnasium import utils
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from gymnasium.envs.mujoco import MujocoEnv
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from gymnasium.spaces import Box
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DEFAULT_CAMERA_CONFIG = {
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"distance": 4.0,
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}
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class HalfCheetahEnv(MujocoEnv, utils.EzPickle):
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r"""
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## Description
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This environment is based on the work of P. Wawrzyński in ["A Cat-Like Robot Real-Time Learning to Run"](http://staff.elka.pw.edu.pl/~pwawrzyn/pub-s/0812_LSCLRR.pdf).
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The HalfCheetah is a 2-dimensional robot consisting of 9 body parts and 8 joints connecting them (including two paws).
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The goal is to apply torque to the joints to make the cheetah run forward (right) as fast as possible, with a positive reward based on the distance moved forward and a negative reward for moving backward.
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The cheetah's torso and head are fixed, and torque can only be applied to the other 6 joints over the front and back thighs (which connect to the torso), the shins (which connect to the thighs), and the feet (which connect to the shins).
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## Action Space
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```{figure} action_space_figures/half_cheetah.png
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:name: half_cheetah
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```
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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 | Type (Unit) |
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| --- | --------------------------------------- | ----------- | ----------- | -------------------------------- | ----- | ------------ |
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| 0 | Torque applied on the back thigh rotor | -1 | 1 | bthigh | hinge | torque (N m) |
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| 1 | Torque applied on the back shin rotor | -1 | 1 | bshin | hinge | torque (N m) |
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| 2 | Torque applied on the back foot rotor | -1 | 1 | bfoot | hinge | torque (N m) |
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| 3 | Torque applied on the front thigh rotor | -1 | 1 | fthigh | hinge | torque (N m) |
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| 4 | Torque applied on the front shin rotor | -1 | 1 | fshin | hinge | torque (N m) |
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| 5 | Torque applied on the front foot rotor | -1 | 1 | ffoot | hinge | torque (N m) |
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## Observation Space
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The observation space consists of the following parts (in order):
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- *qpos (8 elements by default):* Position values of the robot's body parts.
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- *qvel (9 elements):* The velocities of these individual body parts (their derivatives).
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By default, the observation does not include the robot's x-coordinate (`rootx`).
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This can be included by passing `exclude_current_positions_from_observation=False` during construction.
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In this case, the observation space will be a `Box(-Inf, Inf, (18,), float64)`, where the first observation element is the x-coordinate of the robot.
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Regardless of whether `exclude_current_positions_from_observation` is set to `True` or `False`, the x- and y-coordinates are returned in `info` with the keys `"x_position"` and `"y_position"`, respectively.
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By default, however, the observation space is a `Box(-Inf, Inf, (17,), float64)` where the elements are as follows:
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| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Type (Unit) |
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| --- | ------------------------------------------- | ---- | --- | -------------------------------- | ----- | ------------------------ |
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| 0 | z-coordinate of the front tip | -Inf | Inf | rootz | slide | position (m) |
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| 1 | angle of the front tip | -Inf | Inf | rooty | hinge | angle (rad) |
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| 2 | angle of the back thigh | -Inf | Inf | bthigh | hinge | angle (rad) |
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| 3 | angle of the back shin | -Inf | Inf | bshin | hinge | angle (rad) |
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| 4 | angle of the back foot | -Inf | Inf | bfoot | hinge | angle (rad) |
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| 5 | angle of the front thigh | -Inf | Inf | fthigh | hinge | angle (rad) |
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| 6 | angle of the front shin | -Inf | Inf | fshin | hinge | angle (rad) |
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| 7 | angle of the front foot | -Inf | Inf | ffoot | hinge | angle (rad) |
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| 8 | velocity of the x-coordinate of front tip | -Inf | Inf | rootx | slide | velocity (m/s) |
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| 9 | velocity of the z-coordinate of front tip | -Inf | Inf | rootz | slide | velocity (m/s) |
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| 10 | angular velocity of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) |
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| 11 | angular velocity of the back thigh | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) |
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| 12 | angular velocity of the back shin | -Inf | Inf | bshin | hinge | angular velocity (rad/s) |
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| 13 | angular velocity of the back foot | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) |
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| 14 | angular velocity of the front thigh | -Inf | Inf | fthigh | hinge | angular velocity (rad/s) |
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| 15 | angular velocity of the front shin | -Inf | Inf | fshin | hinge | angular velocity (rad/s) |
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| 16 | angular velocity of the front foot | -Inf | Inf | ffoot | hinge | angular velocity (rad/s) |
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| excluded | x-coordinate of the front tip | -Inf | Inf | rootx | slide | position (m) |
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## Rewards
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The total reward is: ***reward*** *=* *forward_reward - ctrl_cost*.
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- *forward_reward*:
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A reward for moving forward,
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this reward would be positive if the Half Cheetah moves forward (in the positive $x$ direction / in the right direction).
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$w_{forward} \times \frac{dx}{dt}$, where
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$dx$ is the displacement of the "tip" ($x_{after-action} - x_{before-action}$),
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$dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $5$),
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and `frametime` which is $0.01$ - so the default is $dt = 5 \times 0.01 = 0.05$,
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$w_{forward}$ is the `forward_reward_weight` (default is $1$).
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- *ctrl_cost*:
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A negative reward to penalize the Half Cheetah for taking actions that are too large.
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$w_{control} \times \|action\|_2^2$,
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where $w_{control}$ is `ctrl_cost_weight` (default is $0.1$).
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`info` contains the individual reward terms.
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## Starting State
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The initial position state is $\mathcal{U}_{[-reset\_noise\_scale \times I_{9}, reset\_noise\_scale \times I_{9}]}$.
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The initial velocity state is $\mathcal{N}(0_{9}, reset\_noise\_scale^2 \times I_{9})$.
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where $\mathcal{N}$ is the multivariate normal distribution and $\mathcal{U}$ is the multivariate uniform continuous distribution.
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## Episode End
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### Termination
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The Half Cheetah never terminates.
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### Truncation
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The default duration of an episode is 1000 timesteps.
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## Arguments
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HalfCheetah provides a range of parameters to modify the observation space, reward function, initial state, and termination condition.
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These parameters can be applied during `gymnasium.make` in the following way:
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```python
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import gymnasium as gym
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env = gym.make('HalfCheetah-v5', 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** | `"half_cheetah.xml"` | Path to a MuJoCo model |
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| `forward_reward_weight` | **float** | `1` | Weight for _forward_reward_ term (see `Rewards` section) |
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| `ctrl_cost_weight` | **float** | `0.1` | Weight for _ctrl_cost_ weight (see `Rewards` section) |
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| `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see `Starting State` section) |
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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 (see `Observation State` section) |
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## Version History
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* v5:
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- Minimum `mujoco` version is now 2.3.3.
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- Added support for fully custom/third party `mujoco` models using the `xml_file` argument (previously only a few changes could be made to the existing models).
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- Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments.
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- Added `env.observation_structure`, a dictionary for specifying the observation space compose (e.g. `qpos`, `qvel`), useful for building tooling and wrappers for the MuJoCo environments.
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- Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`.
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- Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages.
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- Restored the `xml_file` argument (was removed in `v4`).
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- Renamed `info["reward_run"]` to `info["reward_forward"]` to be consistent with the other environments.
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* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3.
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* v3: Support for `gymnasium.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). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
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* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
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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.
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"""
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metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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"rgbd_tuple",
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],
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}
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def __init__(
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self,
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xml_file: str = "half_cheetah.xml",
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frame_skip: int = 5,
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default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
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forward_reward_weight: float = 1.0,
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ctrl_cost_weight: float = 0.1,
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reset_noise_scale: float = 0.1,
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exclude_current_positions_from_observation: bool = True,
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**kwargs,
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):
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utils.EzPickle.__init__(
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self,
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xml_file,
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frame_skip,
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default_camera_config,
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forward_reward_weight,
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ctrl_cost_weight,
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reset_noise_scale,
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exclude_current_positions_from_observation,
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**kwargs,
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)
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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._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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MujocoEnv.__init__(
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self,
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xml_file,
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frame_skip,
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observation_space=None,
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default_camera_config=default_camera_config,
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**kwargs,
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)
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self.metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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"rgbd_tuple",
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],
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"render_fps": int(np.round(1.0 / self.dt)),
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}
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obs_size = (
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self.data.qpos.size
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+ self.data.qvel.size
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- exclude_current_positions_from_observation
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)
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self.observation_space = Box(
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low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64
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)
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self.observation_structure = {
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"skipped_qpos": 1 * exclude_current_positions_from_observation,
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"qpos": self.data.qpos.size
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- 1 * exclude_current_positions_from_observation,
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"qvel": self.data.qvel.size,
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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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def step(self, action):
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x_position_before = self.data.qpos[0]
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self.do_simulation(action, self.frame_skip)
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x_position_after = self.data.qpos[0]
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x_velocity = (x_position_after - x_position_before) / self.dt
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observation = self._get_obs()
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reward, reward_info = self._get_rew(x_velocity, action)
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info = {"x_position": x_position_after, "x_velocity": x_velocity, **reward_info}
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if self.render_mode == "human":
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self.render()
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# truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
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return observation, reward, False, False, info
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def _get_rew(self, x_velocity: float, action):
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forward_reward = self._forward_reward_weight * x_velocity
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ctrl_cost = self.control_cost(action)
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reward = forward_reward - ctrl_cost
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reward_info = {
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"reward_forward": forward_reward,
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"reward_ctrl": -ctrl_cost,
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}
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return reward, reward_info
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def _get_obs(self):
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position = self.data.qpos.flatten()
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velocity = self.data.qvel.flatten()
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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 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 = (
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self.init_qvel
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+ self._reset_noise_scale * self.np_random.standard_normal(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 _get_reset_info(self):
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return {
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"x_position": self.data.qpos[0],
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}
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