A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes

This paper presents a model-free reinforcement learning (RL) algorithm for infinite-horizon average-reward Constrained Markov Decision Processes (CMDPs). Considering a learning horizon K, which is sufficiently large, the proposed algorithm achieves Õ √ SAκ δ K 5 6 regret and zero constraint violation, where S is the number of states, A is the number of actions, and κ and δ are two constants independent of the learning horizon K.

Authors

• Honghao Wei Washington State University
• Xin Liu
• Lei Ying University of Michigan

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