stable_gym.envs.mujoco.humanoid_cost.humanoid_cost
The HumanoidCost gymnasium environment.
Attributes
Classes
Custom Humanoid gymnasium environment. |
Module Contents
- class stable_gym.envs.mujoco.humanoid_cost.humanoid_cost.HumanoidCost(reference_forward_velocity=1.0, randomise_reference_forward_velocity=False, randomise_reference_forward_velocity_range=(0.5, 1.5), forward_velocity_weight=1.0, include_ctrl_cost=False, include_health_penalty=True, health_penalty_size=10, ctrl_cost_weight=0.0001, terminate_when_unhealthy=True, healthy_z_range=(1.0, 2.0), reset_noise_scale=0.01, exclude_current_positions_from_observation=True, exclude_reference_from_observation=False, exclude_reference_error_from_observation=True, exclude_x_velocity_from_observation=False, action_space_dtype=np.float32, observation_space_dtype=np.float64, **kwargs)[source]
Bases:
gymnasium.envs.mujoco.humanoid_v4.HumanoidEnv,gymnasium.utils.EzPickleCustom Humanoid gymnasium environment.
Note
Can also be used in a vectorized manner. See the gym.vector documentation.
- Source:
This is a modified version of the Humanoid Mujoco environment found in the gymnasium library. This modification was first described by Han et al. 2020. Compared to the original Humanoid environment in this modified version:
The objective was changed to a velocity-tracking task. To do this, the reward is replaced with a cost. This cost is the squared difference between the Humanoid’s forward velocity and a reference value (error). Additionally, also a control cost and health penalty can be included in the cost.
Three optional variables were added to the observation space; The reference velocity, the reference error (i.e. the difference between the humanoid’s forward velocity and the reference) and the humanoid’s forward velocity. These variables can be enabled using the
exclude_reference_from_observation,exclude_reference_error_from_observationandexclude_velocity_from_observationenvironment arguments.
The rest of the environment is the same as the original Humanoid environment. Below, the modified cost is described. For more information about the environment (e.g. observation space, action space, episode termination, etc.), please refer to the gymnasium library.
- Modified cost:
A cost, computed using the
HumanoidCost.cost()method, is given for each simulation step, including the terminal step. This cost is defined as the error between the Humanoid’s forward velocity and a reference value. A control cost and health penalty can also be included in the cost. The cost is computed as:\[cost = w_{forward\_velocity} \times (x_{velocity} - x_{reference\_x\_velocity})^2 + w_{ctrl} \times c_{ctrl} + p_{health}\]- Solved Requirements:
Considered solved when the average cost is less than or equal to 50 over 100 consecutive trials.
- How to use:
import stable_gyms import gymnasium as gym env = gym.make("stable_gym:HumanoidCost-v1")
Initialise a new HumanoidCost environment instance.
- Parameters:
reference_forward_velocity (float, optional) – The forward velocity that the agent should try to track. Defaults to
1.0.randomise_reference_forward_velocity (bool, optional) – Whether to randomize the reference forward velocity. Defaults to
False.randomise_reference_forward_velocity_range (tuple, optional) – The range of the random reference forward velocity. Defaults to
(0.5, 1.5).forward_velocity_weight (float, optional) – The weight used to scale the forward velocity error. Defaults to
1.0.include_ctrl_cost (bool, optional) – Whether you also want to penalize the humanoid if it takes actions that are too large. Defaults to
False.include_health_penalty (bool, optional) – Whether to penalize the humanoid if it becomes unhealthy (i.e. if it falls over). Defaults to
True.health_penalty_size (int, optional) – The size of the unhealthy penalty. Defaults to
10.ctrl_cost_weight (float, optional) – The weight used to scale the control cost. Defaults to
1e-4.terminate_when_unhealthy (bool, optional) – Whether to terminate the episode when the humanoid becomes unhealthy. Defaults to
True.healthy_z_range (tuple, optional) – The range of healthy z values. Defaults to
(1.0, 2.0).reset_noise_scale (float, optional) – Scale of random perturbations of the initial position and velocity. Defaults to
1e-2.exclude_current_positions_from_observation (bool, optional) – Whether to omit the x- and y-coordinates of the front tip from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behaviour in policies. Defaults to
True.exclude_reference_from_observation (bool, optional) – Whether the reference should be excluded from the observation. Defaults to
False.exclude_reference_error_from_observation (bool, optional) – Whether the error should be excluded from the observation. Defaults to
True.exclude_x_velocity_from_observation (bool, optional) – Whether to omit the x- component of the velocity from observations. Defaults to
False.action_space_dtype (union[numpy.dtype, str], optional) – The data type of the action space. Defaults to
np.float32.observation_space_dtype (union[numpy.dtype, str], optional) – The data type of the observation space. Defaults to
np.float64.**kwargs – Extra keyword arguments to pass to the
HumanoidEnvclass.
- step(action)[source]
Take step into the environment.
Note
This method overrides the
step()method such that the new cost function is used.- Parameters:
action (np.ndarray) – Action to take in the environment.
- Returns:
tuple containing:
obs (
np.ndarray): Environment observation.cost (
float): Cost of the action.terminated (
bool): Whether the episode is terminated.truncated (
bool): Whether the episode was truncated. This value is set by wrappers when for example a time limit is reached or the agent goes out of bounds.info (
dict): Additional information about the environment.
- Return type:
(tuple)
- reset(seed=None, options=None)[source]
Reset gymnasium environment.
- Parameters:
- Returns:
tuple containing:
obs (
numpy.ndarray): Initial environment observation.info (
dict): Dictionary containing additional information.
- Return type:
(tuple)