Authors
- Takuma Seno
- Michita Imai*
* External authors
Venue
- NeurIPS-2021, Offline RL Workshop
Date
- 2021
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d3rlpy: An Offline Deep Reinforcement Learning Library
Takuma Seno
Michita Imai*
* External authors
NeurIPS-2021, Offline RL Workshop
2021
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.
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