Posterior Sampling-based Online Learning for Episodic POMDPs

Autor:	Tang, Dengwang, Ye, Dongze, Jain, Rahul, Nayyar, Ashutosh, Nuzzo, Pierluigi
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control Statistics - Machine Learning 93E35
Druh dokumentu:	Working Paper
Popis:	Learning in POMDPs is known to be significantly harder than in MDPs. In this paper, we consider the online learning problem for episodic POMDPs with unknown transition and observation models. We propose a Posterior Sampling-based reinforcement learning algorithm for POMDPs (PS4POMDPs), which is much simpler and more implementable compared to state-of-the-art optimism-based online learning algorithms for POMDPs. We show that the Bayesian regret of the proposed algorithm scales as the square root of the number of episodes and is polynomial in the other parameters. In a general setting, the regret scales exponentially in the horizon length $H$, and we show that this is inevitable by providing a lower bound. However, when the POMDP is undercomplete and weakly revealing (a common assumption in the recent literature), we establish a polynomial Bayesian regret bound. We finally propose a posterior sampling algorithm for multi-agent POMDPs, and show it too has sublinear regret. Comment: 41 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.10107 Zobrazit plný text záznamu View this record from Arxiv