d3rlpy: An Offline Deep Reinforcement Learning Library

• URL: http://arxiv.org/abs/2111.03788v1
• Date: Sat, 6 Nov 2021 03:09:39 GMT
• Title: d3rlpy: An Offline Deep Reinforcement Learning Library
• Authors: Takuma Seno, Michita Imai
• Abstract summary: We introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a number of offline deep RL algorithms as well as online algorithms via a user-friendly API.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a number of offline deep RL algorithms as well as online algorithms via a user-friendly API. To assist deep RL research and development projects, d3rlpy provides practical and unique features such as data collection, exporting policies for deployment, preprocessing and postprocessing, distributional Q-functions, multi-step learning and a convenient command-line interface. Furthermore, d3rlpy additionally provides a novel graphical interface that enables users to train offline RL algorithms without coding programs. Lastly, the implemented algorithms are benchmarked with D4RL datasets to ensure the implementation quality. The d3rlpy source code can be found on GitHub: \url{https://github.com/takuseno/d3rlpy}.

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