Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
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.
Cite
Text
Bai et al. "Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I10.28973Markdown
[Bai et al. "Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/bai2024aaai-regret/) doi:10.1609/AAAI.V38I10.28973BibTeX
@inproceedings{bai2024aaai-regret,
title = {{Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes}},
author = {Bai, Qinbo and Mondal, Washim Uddin and Aggarwal, Vaneet},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {10980-10988},
doi = {10.1609/AAAI.V38I10.28973},
url = {https://mlanthology.org/aaai/2024/bai2024aaai-regret/}
}