arXiv.org > cs > arXiv:2002.04017
Computer Science > Machine Learning
arXiv:2002.04017 (cs)
[Submitted on 10 Feb 2020 (v1), last revised 9 Jul 2020 (this version, v3)]
Title:Provable Self-Play Algorithms for Competitive Reinforcement Learning
Authors:Yu Bai, Chi Jin
Abstract: Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment; it remains largely open whether self-play algorithms can be provably effective, especially when it is necessary to manage the exploration/exploitation tradeoff. We study self-play in competitive reinforcement learning under the setting of Markov games, a generalization of Markov decision processes to the two-player case. We introduce a self-play algorithm---Value Iteration with Upper/Lower Confidence Bound (VI-ULCB)---and show that it achieves regret $\tilde{\mathcal{O}}(\sqrt{T})$ after playing $T$ steps of the game, where the regret is measured by the agent's performance against a \emph{fully adversarial} opponent who can exploit the agent's strategy at \emph{any} step. We also introduce an explore-then-exploit style algorithm, which achieves a slightly worse regret of $\tilde{\mathcal{O}}(T^{2/3})$, but is guaranteed to run in polynomial time even in the worst case. To the best of our knowledge, our work presents the first line of provably sample-efficient self-play algorithms for competitive reinforcement learning.
Comments: Appearing at ICML 2020. Fixed typos from v1
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:2002.04017 [cs.LG]
(or arXiv:2002.04017v3 [cs.LG] for this version)
From: Yu Bai [view email]
[v1] Mon, 10 Feb 2020 18:44:50 UTC (60 KB)
[v2] Sun, 23 Feb 2020 22:29:39 UTC (61 KB)
[v3] Thu, 9 Jul 2020 17:07:54 UTC (61 KB)
cs.AI
cs.LG
stat.ML