sinergym.utils.wrappers.EnergyCostWrapper

class sinergym.utils.wrappers.EnergyCostWrapper(*args, **kwargs)

__init__(*args, **kwargs)

Wraps an environment to allow a modular transformation of the step() and reset() methods.

Methods

`__init__`(args, *kwargs)	Wraps an environment to allow a modular transformation of the `step()` and `reset()` methods.
`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.
`has_wrapper_attr`(name)	Checks if the given attribute is within the wrapper or its environment.
`observation`(obs, info)	Build the state observation by adding energy cost information.
`render`()	Uses the `render()` of the `env` that can be overwritten to change the returned data.
`reset`([seed, options])	Resets the environment.
`set_energy_cost_data`()	Sets the cost of energy data used to construct the state observation.
`set_wrapper_attr`(name, value, *[, force])	Sets an attribute on this wrapper or lower environment if name is already defined.
`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.
`np_random_seed`	Returns the base environment's `np_random_seed`.
`observation_space`	Return the `Env` `observation_space` unless overwritten then the wrapper `observation_space` is used.
`render_mode`	Returns the `Env` `render_mode`.
`spec`	Returns the `Env` `spec` attribute with the WrapperSpec if the wrapper inherits from EzPickle.
`unwrapped`	Returns the base environment of the wrapper.

observation(obs: ndarray, info: Dict[str, Any]) → ndarray

Build the state observation by adding energy cost information.

Parameters:

Returns:

Transformed observation.

Return type:

np.ndarray

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

Resets the environment.

Returns:: Tuple with next observation, and dict with information about the environment.
Return type:: Tuple[np.ndarray,Dict[str,Any]]

set_energy_cost_data(): Sets the cost of energy data used to construct the state observation.

step(action: 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 Information about the environment.
Return type:: Tuple[np.ndarray, float, bool, Dict[str, Any]]