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cs.LG2023★ 1 cited
Optimal Sample Complexity for Average Reward Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
We resolve the open question regarding the sample complexity of policy learning for maximizing the long-run average reward associated with a uniformly ergodic Markov decision proce…
math.PR2023
Computable Bounds on Convergence of Markov Chains in Wasserstein Distance via Contractive Drift
Yanlin Qu, Jose Blanchet, Peter Glynn
We introduce a unified framework to estimate the convergence of Markov chains to equilibrium in Wasserstein distance. The framework can provide convergence bounds with rates rangin…
cs.LG2023★ 1 cited
Optimal Sample Complexity of Stable Discounted Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricted minimax sample complexity is kn…