Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs

Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Başar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), the first model-free algorithm for non-stationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of Oe(S 1 3 A3 11 3 HT 2 3 ), where S and A are the numbers of states and actions, respectively, ∆ > 0 is the variation budget, H is the number of time steps per episode, and T is the total number of time steps. We further show that our algorithm is nearly optimal by establishing an information-theoretical lower bound of Ω(S 3 1 A1 31 3 H 2 3 T 3 2 ), the first lower bound in non-stationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efficiency. We further demonstrate the power of our results in the context of multi-agent RL, where non-stationarity is a key challenge.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th International Conference on Machine Learning, ICML 2021
PublisherML Research Press
Pages7447-7458
Number of pages12
ISBN (Electronic)9781713845065
StatePublished - 2021
Externally publishedYes
Event38th International Conference on Machine Learning, ICML 2021 - Virtual, Online
Duration: Jul 18 2021Jul 24 2021

Publication series

NameProceedings of Machine Learning Research
Volume139
ISSN (Electronic)2640-3498

Conference

Conference38th International Conference on Machine Learning, ICML 2021
CityVirtual, Online
Period7/18/217/24/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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