  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}.
