Online Reinforcement Learning for Periodic MDP

Ayush Aniket; Arpan Chattopadhyay

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arXiv · 2207.12045

Online Reinforcement Learning for Periodic MDP

Ayush Aniket·Arpan Chattopadhyay

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Abstract

We study learning in periodic Markov Decision Process(MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period and as sub-linear with the horizon length. Numerical results demonstrate the efficacy of PUCRL2.

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Ayush Aniket, Arpan Chattopadhyay. 2022-07-25. Online Reinforcement Learning for Periodic MDP. https://arxiv.org/abs/2207.12045

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