9 citations · 22 across the 7 of their papers we have counts for
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cs.LG2023
Measuring and Mitigating Interference in Reinforcement Learning
Vincent Liu, Han Wang, Ruo Yu Tao +3
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better.…
cs.LG2023★ 3 cited
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,…
cs.LG2023★ 6 cited
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…