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arXiv · 2006.04354

A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret

Mehdi Jafarnia-Jahromi·Chen-Yu Wei·Rahul Jain·Haipeng Luo
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Abstract

Recently, model-free reinforcement learning has attracted research attention due to its simplicity, memory and computation efficiency, and the flexibility to combine with function approximation. In this paper, we propose Exploration Enhanced Q-learning (EE-QL), a model-free algorithm for infinite-horizon average-reward Markov Decision Processes (MDPs) that achieves regret bound of $O(\sqrt{T})$ for the general class of weakly communicating MDPs, where $T$ is the number of interactions. EE-QL assumes that an online concentrating approximation of the optimal average reward is available. This is the first model-free learning algorithm that achieves $O(\sqrt T)$ regret without the ergodic assumption, and matches the lower bound in terms of $T$ except for logarithmic factors. Experiments show that the proposed algorithm performs as well as the best known model-based algorithms.

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Mehdi Jafarnia-Jahromi, Chen-Yu Wei, Rahul Jain, Haipeng Luo. 2020-06-08. A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret. https://arxiv.org/abs/2006.04354

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