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2022 JMLR JMLR 2022

d3rlpy: An Offline Deep Reinforcement Learning Library

Abstract

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: https://github.com/takuseno/d3rlpy. [abs] [ pdf ][ bib ] [ code ] © JMLR 2022. (edit, beta)

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🧭 Keyword Pioneer — offline deep reinforcement learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy

Authors

Takuma Seno , Michita Imai

Topics

Reinforcement Learning > Methods > Deep RL Reinforcement Learning > Methods > Offline RL Machine Learning > Learning Types > Reinforcement Learning

Keywords

deep reinforcement learning offline reinforcement learning off-policy learning deep rl offline rl offline deep reinforcement learning
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