V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the dm_control suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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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:
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The Room environment - v1 For the documentation of RoomEnv-v0, click the corresponding buttons.
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