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cs.LG2025
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Hao Hu, Xinqi Wang, Simon Shaolei Du
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajector…
cs.LG2024
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
Natalia Zhang, Xinqi Wang, Qiwen Cui +3
We initiate the study of Preference-Based Multi-Agent Reinforcement Learning (PbMARL), exploring both theoretical foundations and empirical validations. We define the task as ident…
cs.LG2024
Distributional Successor Features Enable Zero-Shot Policy Optimization
Chuning Zhu, Xinqi Wang, Tyler Han +2
Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in princi…