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: \url{https://github.com/takuseno/d3rlpy}.

Journal of Machine Learning Research

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Cited by in corpus (3)

• ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
• A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
• OER: Offline Experience Replay for Continual Offline Reinforcement Learning