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GymVectorEnv

Full path: schola.gym.env.GymVectorEnv

schola.gym.env.GymVectorEnv

GymVectorEnv

GymVectorEnv(simulator, protocol, verbosity=0, autoreset_mode=<AutoresetMode SAME_STEP>)

Bases: VectorEnv

A Gym Vector Environment that wraps a Schola Environment.

Parameters

unreal_connection (UnrealConnection) : The connection to the Unreal Engine.

verbosity (int, default=0) : The verbosity level for the environment.

simulator (BaseSimulator)

protocol (BaseRLProtocol)

autoreset_mode (str | MockAutoresetMode)

reset_infos

reset_infos

The information returned from the last reset.

Type

  • List[Dict[str,str]]

Methods

ItemDescription
init
close
get_attrGet an attribute from the environment.
reset
step
unbatch_actionsUnbatch actions from Dict[ObsID,Batched] to a nested Dict[EnvId,Dict[AgentId,Dict[ObsId,Value]]], effectively moving the env, and agent dimensions into Dictionaries.

Attributes

init

__init__(simulator, protocol, verbosity=0, autoreset_mode=<AutoresetMode SAME_STEP>)

Parameters

simulator (BaseSimulator)

protocol (BaseRLProtocol)

verbosity (int)

autoreset_mode (str | MockAutoresetMode)


action_space

action_space: Space

close

close(**kwargs)

get_attr

get_attr(name)

Get an attribute from the environment.

Parameters

name (str) : The name of the attribute to get.

Returns

List[None]

Notes

This method is not implemented and will always return a list of None values, as sub-environments are not individually accessible.


observation_space

observation_space: Space

reset

reset(seed=None, options=None)

Parameters

seed (None | List[int] | int)

options (Dict[str, List[str]] | None)

Returns

Tuple[Dict[str, ndarray], Dict[int, Dict[str, str]]]


single_action_space

single_action_space: Space

single_observation_space

single_observation_space: Space

step

step(actions)

Parameters

actions (Dict[int, ndarray])

Returns

Tuple[Dict[str, ndarray], ndarray, ndarray, ndarray, Dict[int, Dict[str, str]]]


unbatch_actions

unbatch_actions(actions)

Unbatch actions from Dict[ObsID,Batched] to a nested Dict[EnvId,Dict[AgentId,Dict[ObsId,Value]]], effectively moving the env, and agent dimensions into Dictionaries.

Parameters

actions (Dict[int,np.ndarray]) : The batched actions to unbatch.

Returns

The unbatched actions.

Return type: Dict[int,Dict[int,Dict[str,np.ndarray]]]