sinergym.utils.wrappers.MultiObsWrapper

class sinergym.utils.wrappers.MultiObsWrapper(env: EplusEnv, n: int = 5, flatten: bool = True)
__init__(env: EplusEnv, n: int = 5, flatten: bool = True) None

Stack of observations.

Parameters:
  • env (EplusEnv) – Original Gym environment.

  • n (int, optional) – Number of observations to be stacked. Defaults to 5.

  • flatten (bool, optional) – Whether or not flat the observation vector. Defaults to True.

Methods

__init__(env[, n, flatten])

Stack of observations.

class_name()

Returns the class name of the wrapper.

close()

Closes the wrapper and env.

get_wrapper_attr(name)

Gets an attribute from the wrapper and lower environments if name doesn't exist in this object.

render()

Uses the render() of the env that can be overwritten to change the returned data.

reset([seed, options])

Resets the environment.

step(action)

Performs the action in the new environment.

wrapper_spec(**kwargs)

Generates a WrapperSpec for the wrappers.

Attributes

action_space

Return the Env action_space unless overwritten then the wrapper action_space is used.

logger

metadata

Returns the Env metadata.

np_random

Returns the Env np_random attribute.

observation_space

Return the Env observation_space unless overwritten then the wrapper observation_space is used.

render_mode

Returns the Env render_mode.

reward_range

Return the Env reward_range unless overwritten then the wrapper reward_range is used.

spec

Returns the Env spec attribute with the WrapperSpec if the wrapper inherits from EzPickle.

unwrapped

Returns the base environment of the wrapper.

logger = <Logger WRAPPER MultiObsWrapper (INFO)>
reset(seed: int | None = None, options: Dict[str, Any] | None = None) Tuple[ndarray, Dict[str, Any]]

Resets the environment.

Returns:

Stacked previous observations.

Return type:

np.ndarray

step(action: int | ndarray) Tuple[ndarray, float, bool, bool, Dict[str, Any]]

Performs the action in the new environment.

Parameters:

action (Union[int, np.ndarray]) – Action to be executed in environment.

Returns:

Tuple with next observation, reward, bool for terminated episode and dict with extra information.

Return type:

Tuple[np.ndarray, float, bool, Dict[str, Any]]