Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to train agents using pre-collected data, eliminating the need for costly and potentially risky online interactions with the environment. Current research focuses on addressing challenges like distributional shift (mismatch between training and target data) and improving generalization across diverse tasks, employing model architectures such as transformers, convolutional networks, and diffusion models, along with algorithms like conservative Q-learning and decision transformers. These advancements are significant for deploying RL in real-world applications where online learning is impractical or unsafe, impacting fields ranging from robotics and healthcare to personalized recommendations and autonomous systems.
Papers
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
Pai Liu, Lingfeng Zhao, Shivangi Agarwal, Jinghan Liu, Audrey Huang, Philip Amortila, Nan JiangActive Advantage-Aligned Online Reinforcement Learning with Offline Data
Xuefeng Liu, Hung T. C. Le, Siyu Chen, Rick Stevens, Zhuoran Yang, Matthew R. Walter, Yuxin Chen