81 citations · 106 across the 10 of their papers we have counts for
4 papers · 1 filter
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao +2
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior pe…
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
Xiaohan Hu, Yi Ma, Chenjun Xiao +2
One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not…
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Xutong Zhao, Yangchen Pan, Chenjun Xiao +2
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration method that effectively encourages cooperat…
The In-Sample Softmax for Offline Reinforcement Learning
Chenjun Xiao, Han Wang, Yangchen Pan +2
Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however,…