Examples

navground.learning.examples

Corridor

The environment of Corridor with obstacle.

get_env(flat: bool = True, duration: float = 40.0, time_step: float = 0.1) → Env[dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]

Creates the an environment where a single agent traveling along a corridor with a single static obstacle.

Parameters:

• flat (bool) – Whether the observation space is flat

• duration (float) – The duration of an episode

• time_step (float) – The simulation time step

Returns:

A Gymnasium environment

Return type:

Env[dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]

Crossing

The single and multi-agent environments of Crossing.

get_env(flat: bool = True, use_acceleration_action: bool = True, multi_agent: bool = False, **kwargs: Any) → Env[dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] | ParallelEnv[int, dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]

Creates the an environment where 20 agents travel back and forth between way-points, crossing in the middle.

Parameters:

• flat (bool) – Whether the observation space is flat

• use_acceleration_action (bool) – Whether actions are acceleration or velocities

• multi_agent (bool) – Whether to expose all agents or just one.

• kwargs (Any) – Arguments passed to the environment constructor

Returns:

A Parallel PettingZoo environment if multi_agent is set, else a Gymnasium environment.

Return type:

Env[dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] | ParallelEnv[int, dict[str, ndarray[Any, dtype[Any]]] | ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]