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cs.LG2025
Behavioral Exploration: Learning to Explore via In-Context Adaptation
Andrew Wagenmaker, Zhiyuan Zhou, Sergey Levine
Developing autonomous agents that quickly explore an environment and adapt their behavior online is a canonical challenge in robotics and machine learning. While humans are able to…
cs.LG2025
Reinforcement Learning with Action Chunking
Qiyang Li, Zhiyuan Zhou, Sergey Levine
We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks. Our recipe is designed for the offl…
cs.LG2025
Flow Q-Learning
Seohong Park, Qiyang Li, Sergey Levine
We present flow Q-learning (FQL), a simple and performant offline reinforcement learning (RL) method that leverages an expressive flow-matching policy to model arbitrarily complex…