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cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
Improving Learning to Optimize Using Parameter Symmetries
Guy Zamir, Aryan Dokania, Bo Zhao +1
We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry tra…