Python PettingZoo API Documentation
mlagents_envs.envs.pettingzoo_env_factory
PettingZooEnvFactory Objects
class PettingZooEnvFactory()
env
| env(seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]) -> UnityAECEnv
Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper
Arguments:
seed: The seed for the action spaces of the agents.kwargs: Any argument accepted byUnityEnvironmentclass except file_name
mlagents_envs.envs.unity_aec_env
UnityAECEnv Objects
class UnityAECEnv(UnityPettingzooBaseEnv, AECEnv)
Unity AEC (PettingZoo) environment wrapper.
__init__
| __init__(env: BaseEnv, seed: Optional[int] = None)
Initializes a Unity AEC environment wrapper.
Arguments:
env: The UnityEnvironment that is being wrapped.seed: The seed for the action spaces of the agents.
step
| step(action: Any) -> None
Sets the action of the active agent and get the observation, reward, termination, truncation and info of the next agent.
Arguments:
action: The action for the active agent
observe
| observe(agent_id)
Returns the observation an agent currently can make. last() calls this function.
last
| last(observe=True)
returns observation, cumulative reward, termination, truncation, info for the current agent (specified by self.agent_selection)
mlagents_envs.envs.unity_parallel_env
UnityParallelEnv Objects
class UnityParallelEnv(UnityPettingzooBaseEnv, ParallelEnv)
Unity Parallel (PettingZoo) environment wrapper.
__init__
| __init__(env: BaseEnv, seed: Optional[int] = None)
Initializes a Unity Parallel environment wrapper.
Arguments:
env: The UnityEnvironment that is being wrapped.seed: The seed for the action spaces of the agents.
reset
| reset() -> Dict[str, Any]
Resets the environment.
mlagents_envs.envs.unity_pettingzoo_base_env
UnityPettingzooBaseEnv Objects
class UnityPettingzooBaseEnv()
Unity Petting Zoo base environment.
observation_spaces
| @property
| observation_spaces() -> Dict[str, spaces.Space]
Return the observation spaces of all the agents.
observation_space
| observation_space(agent: str) -> Optional[spaces.Space]
The observation space of the current agent.
action_spaces
| @property
| action_spaces() -> Dict[str, spaces.Space]
Return the action spaces of all the agents.
action_space
| action_space(agent: str) -> Optional[spaces.Space]
The action space of the current agent.
side_channel
| @property
| side_channel() -> Dict[str, Any]
The side channels of the environment. You can access the side channels of an environment with env.side_channel[<name-of-channel>].
reset
| reset(seed: Optional[int] = None, options: Optional[Dict] = None) -> Any
Resets the environment.
render
| render()
NOT SUPPORTED.
Renders the environment as specified by self.render_mode, if supported.
Render mode can be human to display a window. Other render modes in the default environments are 'rgb_array' which returns a numpy array and is supported by all environments outside of classic, and 'ansi' which returns the strings printed (specific to classic environments).
close
| close() -> None
Close the environment.