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.

reset(seed: int | None = None, options: Dict[str, Any] | None = None) → Tuple[ndarray, Dict[str, Any]]

Resets the environment.

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]]