README.md

space-wrappers

General purpose environment wrappers for openai gym.

List of currently implemented Wrappers

For a description of what these wrappers do, please look at their respective docstrings.

Action Wrappers

• FlattenedActionWrapper
• DiscretizedActionWrapper
• RescaledActionWrapper
• ToScalarActionWrapper
• RepeatActionWrapper

Observation Wrappers

• FlattenedObservationWrapper
• DiscretizedObservationWrapper
• RescaledObservationWrapper
• StackObservationWrapper

Misc

• ContinuingEnvWrapper

Usage Example

Suppose you want to train a (D)DQN agent for an environment with continuous actions. Since DQN implementations typically expect to produce a single discrete action, the action space has to both be discretized and flattened, as demonstrated in the code below.

import gym
import space_wrappers
# An environment with a continuous action space.
# We first turn it into a MultiDiscrete, and then into
# a flat discrete action space.
env = gym.make("LunarLanderContinuous-v2")
wrapped = space_wrappers.DiscretizedActionWrapper(env, 3)
wrapped = space_wrappers.FlattenedActionWrapper(wrapped)

# this is now a single integer
print(wrapped.action_space.sample())

TODO

• Documentation
• Handle Tuple spaces
• More Sanity checks
• Image transformations (resample, resize, ...)
• Array Transformations (reshape)
• allow pickling of wrapped envs + corresponding tests