Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Abstraction: Survey of offline RL algorithms learning policies from static datasets without online interaction

Key points:

• Offline (batch) RL: trains policies from previously collected data only — no additional environment interaction — enabling use of large historical datasets
• Motivated by high-stakes domains where online data collection is costly or dangerous: healthcare, education, robotics
• Core challenge: distribution shift between the behavior policy (data collection) and learned policy leads to overestimation of out-of-distribution actions
• Reviews current deep RL-based offline methods and their limitations; describes potential solutions to mitigate distributional shift
• Covers applications and open problems; paper by Levine, Kumar, Tucker, and Fu (2020), arXiv:2005.01643
• Positioned as a conceptual tutorial to help new researchers enter the field

Connections: Sergey Levine · Reinforcement Learning · Offline Reinforcement Learning

Source: https://arxiv.org/abs/2005.01643