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Learning Infinite-horizon Average-reward Markov Decision Process with Constraints
Liyu Chen · Rahul Jain · Haipeng Luo

Tue Jul 19 08:20 AM -- 08:25 AM (PDT) @ Hall F
We study regret minimization for infinite-horizon average-reward Markov Decision Processes (MDPs) under cost constraints.We start by designing a policy optimization algorithm with carefully designed action-value estimator and bonus term,and show that for ergodic MDPs, our algorithm ensures $O(\sqrt{T})$ regret and constant constraint violation, where $T$ is the total number of time steps.This strictly improves over the algorithm of (Singh et al., 2020), whose regret and constraint violation are both $O(T^{2/3})$.Next, we consider the most general class of weakly communicating MDPs. Through a finite-horizon approximation, we develop another algorithm with $O(T^{2/3})$ regret and constraint violation, which can be further improved to $O(\sqrt{T})$ via a simple modification,albeit making the algorithm computationally inefficient.As far as we know, these are the first set of provable algorithms for weakly communicating MDPs with cost constraints.

Author Information

Liyu Chen (USC)
Rahul Jain (USC)
Haipeng Luo (University of Southern California)

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