sinergym.utils.wrappers.VariabilityContextWrapper

class sinergym.utils.wrappers.VariabilityContextWrapper(*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.
`render`()	Uses the `render()` of the `env` that can be overwritten to change the returned data.
`reset`(*[, seed, options])	Uses the `reset()` of the `env` that can be overwritten to change the returned data.
`set_wrapper_attr`(name, value, *[, force])	Sets an attribute on this wrapper or lower environment if name is already defined.
`step`(action)	Executes an action and updates the environment's context if needed.
`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.

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

Executes an action and updates the environment’s context if needed.

Parameters:: action (np.ndarray) – Action selected by the agent.
Returns:: Observation for next timestep, reward obtained, Whether the episode has ended or not, Whether episode has been truncated or not, and a dictionary with extra information
Return type:: Tuple[np.ndarray, float, bool, Dict[str, Any]]