离线强化学习（OfflineRL）是强化学习中的一种设置，旨在仅使用静态数据集（以前由行为策略收集）学习好的策略，而不需要在部署环境中进行进一步的交互。OfflineRL近年来受到广泛关注，作为在线采样成本敏感应用中强化学习的潜在范例。

Dataset

D4RLContinuous Control https://github.com/rail-berkeley/d4rl

NeoRLNear Real World https://github.com/siemens/industrialbenchmark/tree/master/industrial_benchmark_python

RoboNetvision, robotics http://www.robonet.wiki/

Benchmarks

rl_unplugged RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged

d3rlpyd3rlpy: An Offline Deep Reinforcement Learning Library https://github.com/takuseno/d3rlpy

offlinerl https://github.com/polixir/OfflineRL
tianshou https://github.com/thu-ml/tianshou
revive https://revive.cn/

Other related settings

2. Model-Based

3. Theory

4. Other related settings

A list of competitions that is of interest to the community. (sorted by starting date)

Real-world Reinforcement Learning Challenge—Learning to make fair and incentive coupon decisions for sales promotion from data, organized by Polixir, Dec. 25, 2021 – Feb. 27, 2022 (Ongoing)
MineRL BASALT Challenge NeurIPS 2021 Competition—Learning from Human Feedback in Minecraft, organized by C.H.A.I. – UC Berkeley, July 7, 2021 – Dec 14, 2021
MineRL Diamond Challenge NeurIPS 2021 Competition—Training Sample-Efficient Agents in Minecraft, organized by MineRL Labs – Carnegie Mellon University, Jun. 9, 2021 – Dec., 2021
Tactile Games Playtest Agent—Level Difficulty Prediction of Lily’s Garden Levels, at 3rd IEEE Conference on Games in 2021, organized by Tactile Games
Real Robot Challenge, organized by Empirical Inference Max Planck Institute for Intelligent Systems, May 28, 2021 – Sep. 16, 2021
Real Robot Challenge, organized by Empirical Inference Max Planck Institute for Intelligent Systems, Aug. 10, 2020 – Dec. 14, 2020
MineRL NeurIPS 2020 Competition—Sample-efficient reinforcement learning in Minecraft, organized by MineRL Labs – Carnegie Mellon University, Jul. 1, 2020 – Dec. 5, 2020
MineRL NeurIPS 2019 Competition—Sample-efficient reinforcement learning in Minecraft, organized by MineRL Labs – Carnegie Mellon University, May 10, 2019 – Dec 14, 2019

本文转载自： https://offlinerl.ai/

感谢南京大学、南栖仙策在离线强化学习领域的贡献