POPGym is designed to benchmark memory in deep reinforcement learning. It contains a set of environments and a collection of memory model baselines. The environments are all Partially Observable Markov Decision Process (POMDP) environments following the Openai Gym interface. Our environments follow a few basic tenets:
popgym
environments require only gym
, numpy
, and mazelib
as dependenciesThe paper uses 15M environment steps for each trial.
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