610 citations · 2k across the 74 of their papers we have counts for
29 papers · 1 filter
Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs
Guy Zamir, Matthew Zurek, Yudong Chen
Online reinforcement learning in infinite-horizon Markov decision processes (MDPs) remains less theoretically and algorithmically developed than its episodic counterpart, with many…
Optimal Single-Policy Sample Complexity and Transient Coverage for Average-Reward Offline RL
Matthew Zurek, Guy Zamir, Yudong Chen
We study offline reinforcement learning in average-reward MDPs, which presents increased challenges from the perspectives of distribution shift and non-uniform coverage, and has be…
Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL
Matthew Zurek, Yudong Chen
We study the sample complexity of finding an -optimal policy in average-reward Markov Decision Processes (MDPs) with a generative model. The minimax optimal span-based…
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
Tyler Sam, Yudong Chen, Christina Lee Yu
Many reinforcement learning (RL) algorithms are too costly to use in practice due to the large sizes of the problem's state and action space. To resolve this issue, we study…
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
Matthew Zurek, Yudong Chen
We study the sample complexity of the plug-in approach for learning -optimal policies in average-reward Markov decision processes (MDPs) with a generative model. The p…
Stable Offline Value Function Learning with Bisimulation-based Representations
Brahma S. Pavse, Yudong Chen, Qiaomin Xie +1
In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each state when taking actio…