AAAI 2024technical17 citations
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
Abstract
In this paper, we consider an infinite horizon average reward Markov Decision Process (MDP). Distinguishing itself from existing works within this context, our approach harnesses the power of the general policy gradient-based algorithm, liberating it from the constraints of assuming a linear MDP structure. We propose a vanilla policy gradient-based algorithm and show its global convergence property. We then prove that the proposed algorithm has O(T^3/4) regret. Remarkably, this paper marks a pioneering effort by presenting the first exploration into regret bound computation for the general parameterized policy gradient algorithm in the context of average reward scenarios.
BibTeX
@article{Bai_Mondal_Aggarwal_2024, title={Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28973}, DOI={10.1609/aaai.v38i10.28973}, abstractNote={In this paper, we consider an infinite horizon average reward Markov Decision Process (MDP). Distinguishing itself from existing works within this context, our approach harnesses the power of the general policy gradient-based algorithm, liberating it from the constraints of assuming a linear MDP structure. We propose a vanilla policy gradient-based algorithm and show its global convergence property. We then prove that the proposed algorithm has O(T^3/4) regret. Remarkably, this paper marks a pioneering effort by presenting the first exploration into regret bound computation for the general parameterized policy gradient algorithm in the context of average reward scenarios.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bai, Qinbo and Mondal, Washim Uddin and Aggarwal, Vaneet}, year={2024}, month={Mar.}, pages={10980-10988} }