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Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average Reward Markov Decision Processes
April 3, 2024, 4:42 a.m. | Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
cs.LG updates on arXiv.org arxiv.org
Abstract: We present two Policy Gradient-based methods with general parameterization in the context of infinite horizon average reward Markov Decision Processes. The first approach employs Implicit Gradient Transport for variance reduction, ensuring an expected regret of the order $\tilde{\mathcal{O}}(T^{3/5})$. The second approach, rooted in Hessian-based techniques, ensures an expected regret of the order $\tilde{\mathcal{O}}(\sqrt{T})$. These results significantly improve the state of the art of the problem, which achieves a regret of $\tilde{\mathcal{O}}(T^{3/4})$.
abstract arxiv context cs.lg decision general gradient horizon markov policy processes transport type variance
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