Environment Wrappers

This page contains the reference documentation for environment wrappers. Here you can find a more extensive write up on how to work with these.

Interfaces and Utilities

These are the wrapper interfaces, base classes and interfaces:

Wrapper

A transparent environment Wrapper that works with any manifestation of BaseEnv.

Types of Wrappers:

ObservationWrapper	A Wrapper with typing support modifying the environments observation.
ActionWrapper	A Wrapper with typing support modifying the agents action.
RewardWrapper	A Wrapper with typing support modifying the reward before passed to the agent.
WrapperRegistry	Handles dynamic registration of Wrapper sub-classes.

Built-in Wrappers

Below you find the reference documentation for environment wrappers.

General Wrappers:

LogStatsWrapper	A statistics logging wrapper for BaseEnv.
ObservationLoggingWrapper	A observation logging wrapper for BaseEnv.
TimeLimitWrapper	Wrapper to limit the environment step count, equivalent to gym.wrappers.time_limit.
RandomResetWrapper	A wrapper skipping the first few steps by taking random actions.
SortedSpacesWrapper	This class wraps a given StructuredEnvSpacesMixin env to ensure that all observation- and action-spaces are sorted alphabetically.
NoDictSpacesWrapper	Wraps observations and actions by replacing dictionary spaces with the sole contained sub-space.

ObservationWrappers:

DictObservationWrapper	Wraps a single observation into a dictionary space.
ObservationLoggingWrapper	A observation logging wrapper for BaseEnv.
ObservationStackWrapper	An wrapper stacking the observations of multiple subsequent time steps.
NoDictObservationWrapper	Wraps observations by replacing the dictionary observation space with the sole contained sub-space.

ActionWrappers:

DictActionWrapper	Wraps either a single action space or a tuple action space into dictionary space.
NoDictActionWrapper	Wraps actions by replacing the dictionary action space with the sole contained sub-space.
SplitActionsWrapper	Splits an actions into separate ones.
DiscretizeActionsWrapper	The DiscretizeActionsWrapper provides functionality for discretizing individual continuous actions into discrete

RewardWrappers:

RewardScalingWrapper	Scales original step reward by a multiplicative scaling factor.
RewardClippingWrapper	Clips original step reward to range [min, max].

Observation Pre-Processing Wrapper

Below you find the reference documentation for observation pre-processing. Here you can find a more extensive write up on how to work with the observation pre-processing package.

These are interfaces and components required for observation pre-processing:

PreProcessingWrapper	An observation pre-processing wrapper.
PreProcessor	Interface for observation pre-processors.

These are the available built-in maze.pre_processors compatible with the PreProcessingWrapper:

FlattenPreProcessor	An array flattening pre-processor.
OneHotPreProcessor	An one-hot encoding pre-processor for categorical features.
ResizeImgPreProcessor	An image resizing pre-processor.
TransposePreProcessor	An array transposition pre-processor.
UnSqueezePreProcessor	An un-squeeze pre-processor.
Rgb2GrayPreProcessor	An rgb-to-gray-scale conversion pre-processor.

Observation Normalization Wrapper

Below you find the reference documentation for observation normalization. Here you can find a more extensive write up on how to work with the observation normalization package.

These are interfaces and utility functions required for observation normalization:

ObservationNormalizationWrapper	An observation normalization wrapper.
ObservationNormalizationStrategy	Abstract base class for normalization strategies.
obtain_normalization_statistics	Obtain the normalization statistics of a given environment.
estimate_observation_normalization_statistics	Helper function estimating normalization statistics.
make_normalized_env_factory	Wrap an existing env factory to assign the passed normalization statistics.

These are the available built-in maze.normalization_strategies compatible with the ObservationNormalizationWrapper:

MeanZeroStdOneObservationNormalizationStrategy	Normalizes observations to have zero mean and standard deviation one.
RangeZeroOneObservationNormalizationStrategy	Normalizes observations to value range [0, 1].

Gym Environment Wrapper

Below you find the reference documentation for wrapping gym environments. Here you can find a more extensive write up on how to integrate Gym environments within Maze.

These are the contained components:

GymMazeEnv	Wraps a Gym env into a Maze environment.
make_gym_maze_env	Initializes a GymMazeEnv by registered Gym env name (id).
GymCoreEnv	Wraps a Gym environment into a maze core environment.
GymRenderer	A Maze-compatible Gym renderer.
GymRewardAggregator	A dummy reward aggregation object simply repeating the environment’s original reward.
GymObservationConversion	A dummy conversion interface asserting that the observation is packed into a dictionary space.
GymActionConversion	A dummy conversion interface asserting that the action is packed into a dictionary space.