__credits__ = ["Kallinteris-Andreas", "Rushiv Arora"] import numpy as np from gymnasium import utils from gymnasium.envs.mujoco import MujocoEnv from gymnasium.spaces import Box DEFAULT_CAMERA_CONFIG = { "distance": 4.0, } class HalfCheetahEnv(MujocoEnv, utils.EzPickle): r""" ## Description 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). The HalfCheetah is a 2-dimensional robot consisting of 9 body parts and 8 joints connecting them (including two paws). 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. 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). ## Action Space ```{figure} action_space_figures/half_cheetah.png :name: half_cheetah ``` 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 | Type (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 observation space consists of the following parts (in order): - *qpos (8 elements by default):* Position values of the robot's body parts. - *qvel (9 elements):* The velocities of these individual body parts (their derivatives). By default, the observation does not include the robot's x-coordinate (`rootx`). This can be included by passing `exclude_current_positions_from_observation=False` during construction. 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. 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. By default, however, the observation space is a `Box(-Inf, Inf, (17,), float64)` where the elements are as follows: | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Type (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 back thigh | -Inf | Inf | bthigh | hinge | angle (rad) | | 3 | angle of the back shin | -Inf | Inf | bshin | hinge | angle (rad) | | 4 | angle of the back foot | -Inf | Inf | bfoot | hinge | angle (rad) | | 5 | angle of the front thigh | -Inf | Inf | fthigh | hinge | angle (rad) | | 6 | angle of the front shin | -Inf | Inf | fshin | hinge | angle (rad) | | 7 | angle of the front foot | -Inf | Inf | ffoot | hinge | angle (rad) | | 8 | velocity of the x-coordinate of front tip | -Inf | Inf | rootx | slide | velocity (m/s) | | 9 | velocity of the z-coordinate of front tip | -Inf | Inf | rootz | slide | velocity (m/s) | | 10 | angular velocity of the front tip | -Inf | Inf | rooty | hinge | angular velocity (rad/s) | | 11 | angular velocity of the back thigh | -Inf | Inf | bthigh | hinge | angular velocity (rad/s) | | 12 | angular velocity of the back shin | -Inf | Inf | bshin | hinge | angular velocity (rad/s) | | 13 | angular velocity of the back foot | -Inf | Inf | bfoot | hinge | angular velocity (rad/s) | | 14 | angular velocity of the front thigh | -Inf | Inf | fthigh | hinge | angular velocity (rad/s) | | 15 | angular velocity of the front shin | -Inf | Inf | fshin | hinge | angular velocity (rad/s) | | 16 | angular velocity of the front foot | -Inf | Inf | ffoot | hinge | angular velocity (rad/s) | | excluded | x-coordinate of the front tip | -Inf | Inf | rootx | slide | position (m) | ## Rewards The total reward is: ***reward*** *=* *forward_reward - ctrl_cost*. - *forward_reward*: A reward for moving forward, this reward would be positive if the Half Cheetah moves forward (in the positive $x$ direction / in the right direction). $w_{forward} \times \frac{dx}{dt}$, where $dx$ is the displacement of the "tip" ($x_{after-action} - x_{before-action}$), $dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $5$), and `frametime` which is $0.01$ - so the default is $dt = 5 \times 0.01 = 0.05$, $w_{forward}$ is the `forward_reward_weight` (default is $1$). - *ctrl_cost*: A negative reward to penalize the Half Cheetah for taking actions that are too large. $w_{control} \times \|action\|_2^2$, where $w_{control}$ is `ctrl_cost_weight` (default is $0.1$). `info` contains the individual reward terms. ## Starting State The initial position state is $\mathcal{U}_{[-reset\_noise\_scale \times I_{9}, reset\_noise\_scale \times I_{9}]}$. The initial velocity state is $\mathcal{N}(0_{9}, reset\_noise\_scale^2 \times I_{9})$. where $\mathcal{N}$ is the multivariate normal distribution and $\mathcal{U}$ is the multivariate uniform continuous distribution. ## Episode End ### Termination The Half Cheetah never terminates. ### Truncation The default duration of an episode is 1000 timesteps. ## Arguments HalfCheetah provides a range of parameters to modify the observation space, reward function, initial state, and termination condition. These parameters can be applied during `gymnasium.make` in the following way: ```python import gymnasium as gym env = gym.make('HalfCheetah-v5', 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` | Weight for _forward_reward_ term (see `Rewards` section) | | `ctrl_cost_weight` | **float** | `0.1` | Weight for _ctrl_cost_ weight (see `Rewards` section) | | `reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see `Starting State` section) | | `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) | ## Version History * v5: - Minimum `mujoco` version is now 2.3.3. - 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). - Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments. - 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. - Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`. - Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages. - Restored the `xml_file` argument (was removed in `v4`). - Renamed `info["reward_run"]` to `info["reward_forward"]` to be consistent with the other environments. * v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3. * 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). * v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics). * v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments. * v0: Initial versions release. """ metadata = { "render_modes": [ "human", "rgb_array", "depth_array", "rgbd_tuple", ], } def __init__( self, xml_file: str = "half_cheetah.xml", frame_skip: int = 5, default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG, forward_reward_weight: float = 1.0, ctrl_cost_weight: float = 0.1, reset_noise_scale: float = 0.1, exclude_current_positions_from_observation: bool = True, **kwargs, ): utils.EzPickle.__init__( self, xml_file, frame_skip, default_camera_config, forward_reward_weight, ctrl_cost_weight, reset_noise_scale, exclude_current_positions_from_observation, **kwargs, ) 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 ) MujocoEnv.__init__( self, xml_file, frame_skip, observation_space=None, default_camera_config=default_camera_config, **kwargs, ) self.metadata = { "render_modes": [ "human", "rgb_array", "depth_array", "rgbd_tuple", ], "render_fps": int(np.round(1.0 / self.dt)), } obs_size = ( self.data.qpos.size + self.data.qvel.size - exclude_current_positions_from_observation ) self.observation_space = Box( low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64 ) self.observation_structure = { "skipped_qpos": 1 * exclude_current_positions_from_observation, "qpos": self.data.qpos.size - 1 * exclude_current_positions_from_observation, "qvel": self.data.qvel.size, } 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 observation = self._get_obs() reward, reward_info = self._get_rew(x_velocity, action) info = {"x_position": x_position_after, "x_velocity": x_velocity, **reward_info} if self.render_mode == "human": self.render() # truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make` return observation, reward, False, False, info def _get_rew(self, x_velocity: float, action): forward_reward = self._forward_reward_weight * x_velocity ctrl_cost = self.control_cost(action) reward = forward_reward - ctrl_cost reward_info = { "reward_forward": forward_reward, "reward_ctrl": -ctrl_cost, } return reward, reward_info def _get_obs(self): position = self.data.qpos.flatten() velocity = self.data.qvel.flatten() 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 _get_reset_info(self): return { "x_position": self.data.qpos[0], }