GenesisEnv

class genesis_forge.GenesisEnv(num_envs: int = 1, dt: float = 0.01, max_episode_length_sec: int | None = 10, max_episode_random_scaling: float = 0.0, extras_logging_key: str = 'episode')

Bases: object

Base environment class for your simulated robot environment.

Parameters:

• num_envs – Number of parallel environments.

• dt – Simulation time step.

• max_episode_length_sec – Maximum episode length in seconds.

• max_episode_random_scaling – Scale the maximum episode length by this amount (+/-) so that not all environments reset at the same time.

• extras_logging_key – The key used, in info/extras dict, which is returned by step and reset functions, to send data to tensorboard by the RL agent.

Example:

class MyEnv(GenesisEnv):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # ...Define scene here...
        self.scene = gs.Scene()
        self.terrain = self.scene.add_entity(gs.morphs.Plane())
        self.robot = self.scene.add_entity( ... )

    def step(self, actions: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, dict[str, Any]]:
        # ...step logic here...
        return obs, rewards, terminations, truncations, info

    def reset(self, envs_idx: list[int] = None) -> tuple[torch.Tensor, dict[str, Any]]:
        # ...reset logic here...
        return obs, info

    def get_observations(self) -> torch.Tensor:
        # ...define current observations here...
        return obs

build() → None

Builds the environment before the first step. The Genesis scene and all the scene entities must be added before calling this method.

close()

Close the environment.

get_observations() → torch.Tensor

Returns the current observations for each environment. Override this method to return the observations for your environment.

Example:

def get_observations(self) -> torch.Tensor:
    return torch.cat(
    [
        self.base_ang_vel * self.obs_scales["ang_vel"],  # 3
        self.projected_gravity,  # 3
        self.commands * self.commands_scale,  # 3
        (self.dof_pos - self.default_dof_pos) * self.obs_scales["dof_pos"],  # 12
        self.dof_vel * self.obs_scales["dof_vel"],  # 12
        self.actions,  # 12
    ],
    axis=-1,
)

reset(envs_idx: list[int] = None) → tuple[torch.Tensor, dict[str, Any]]

Reset one or more environments. Each of the registered managers will also be reset for those environments.

Parameters:

env_ids – The environment ids to reset. If None, all environments are reset.

Returns:

A batch of observations and info from the vectorized environment.

set_max_episode_length(max_episode_length_sec: int) → int

Set or change the maximum episode length.

Parameters:

max_episode_length_sec – The maximum episode length in seconds.

Returns:

The maximum episode length in steps.

step(actions: torch.Tensor) → tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, dict[str, Any]]

Performs a step in all environments with the given actions.

Parameters:

actions – Batch of actions for each environment with the action_space shape.

Returns:

Batch of (observations, rewards, terminations, truncations, info/extras)

action_space: spaces.Space | None = None

property actions: torch.Tensor

The actions for each environment for this step. If you’re using an action manager, these are the actions prior to being handled by the action manager.

can_be_wrapped: bool = True

property extras: dict

The extras/infos dictionary reset at the start of every step, and contains additional data about the environment during that step.

property last_actions: torch.Tensor

The actions for for the previous step.

property max_episode_length_sec: int | None

The max episode length, in seconds, for each environment.

property max_episode_length_steps: int | None

The max episode length, in steps, for each environment. If episode randomization scaling is enabled, this will be the base max episode length before scaling.

property num_actions: int

The number of actions for each environment.

property num_observations: int

The number of observations for each environment.

observation_space: spaces.Space | None = None

property step_dt: float

The time step of the environment. This is an alias of the dt property.

property unwrapped

Returns this environment, not a wrapped version of it.