sinergym.utils.wrappers.WeatherForecastingWrapper

class sinergym.utils.wrappers.WeatherForecastingWrapper(*args, **kwargs)

__init__(*args, **kwargs)

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

Parameters:

env – The environment to wrap

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 weather forecast information.
render()	Uses the render() of the env that can be overwritten to change the returned data.
reset([seed, options])	Resets the environment.
set_forecast_data()	Set the weather data used to build de 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.

logger = <Logger WRAPPER WeatherForecastingWrapper (INFO)>

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

Build the state observation by adding weather forecast information.

Parameters:

• obs (np.ndarray) – Original observation.

• info (Dict[str, Any]) – Information about the environment.

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_forecast_data() → None

Set the weather data used to build de state observation. If forecast_variability is not None, it applies Ornstein-Uhlenbeck process to the data.

step(action: ndarray) → Tuple[ndarray, SupportsFloat, bool, bool, Dict[str, Any]]

Performs the action in the new environment.

Parameters:

action (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, SupportsFloat, bool, bool, Dict[str, Any]]