Papers › d3rlpy: An Offline Deep Reinforcement Learning Library

d3rlpy: An Offline Deep Reinforcement Learning Library

6 Nov 2021arXiv:2111.03788archive 2025-07-28

Takuma Seno, Michita Imai

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: \url{https://github.com/takuseno/d3rlpy}.

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takuseno/d3rlpy officialmentioned in papermentioned on GitHubpytorchMIT report

shyamal-anadkat/offlinerl mentioned on GitHubpytorchMIT report

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D4RL Deep Reinforcement Learning Offline RL Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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