sinergym.utils.wrappers.ProbabilisticContextWrapper

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

Wrapper that probabilistically updates context variables at each step.

This wrapper provides a unified approach to context updates with multiple modes:

• Probabilistic updates: Each step has a probability of triggering a context update

• Multiple update modes: Same value for all variables, independent values, or probabilistic per-variable updates

• Support for both absolute values and delta-based increments to current context values

__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_obs_dict(obs)	Convert observation array to dictionary with variable names as keys.
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(**kwargs)	Resets the environment and reinitializes current context.
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 probabilistically updates context if triggered.
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 ProbabilisticContextWrapper (INFO)>

reset(**kwargs) → Tuple[ndarray, Dict[str, Any]]

Resets the environment and reinitializes current context.

Parameters:

**kwargs – Additional arguments passed to the underlying environment’s reset method.

Returns:

Standard Gymnasium reset return containing:

• Initial observation

• Info dictionary

Return type:

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

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

Executes an action and probabilistically updates context if triggered.

The update behavior depends on the type of update_probability:

• If update_probability is a float: At each step, there’s a probability that a context update event occurs. When it does, all context variables are updated together according to the configured mode and type.

• If update_probability is a list: In each step, each context variable is independently evaluated according to its probability. Variables that pass their probability check are updated according to the configured mode and type.

Parameters:

action (np.ndarray) – Action selected by the agent.

Returns:

Standard Gymnasium step

return containing:

• Observation for next timestep

• Reward obtained

• Whether the episode has ended (terminated)

• Whether episode has been truncated

• Dictionary with extra information

Return type:

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