Online Learning for Unknown Partially Observable MDPs

02/25/2021 ∙ by Mehdi Jafarnia-Jahromi, et al. ∙ 0

Solving Partially Observable Markov Decision Processes (POMDPs) is hard. Learning optimal controllers for POMDPs when the model is unknown is harder. Online learning of optimal controllers for unknown POMDPs, which requires efficient learning using regret-minimizing algorithms that effectively tradeoff exploration and exploitation, is even harder, and no solution exists currently. In this paper, we consider infinite-horizon average-cost POMDPs with unknown transition model, though known observation model. We propose a natural posterior sampling-based reinforcement learning algorithm (POMDP-PSRL) and show that it achieves O(T^2/3) regret where T is the time horizon. To the best of our knowledge, this is the first online RL algorithm for POMDPs and has sub-linear regret.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.