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arXiv · 2309.15737

Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need

Danil Provodin·Pratik Gajane·Mykola Pechenizkiy·Maurits Kaptein
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Abstract

We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of \tilde{O} (HS \sqrt{AT}) for any communicating CMDP with S states, A actions, and bound on the hitting time H. This regret bound matches the lower bound in order of time horizon T and is the best-known regret bound for communicating CMDPs in the infinite-horizon undiscounted setting. Empirical results show that, despite its simplicity, our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning.

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Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein. 2023-09-27. Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need. https://arxiv.org/abs/2309.15737

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