Computer Science > Machine Learning
[Submitted on 10 Jun 2021 (v1), last revised 20 Dec 2022 (this version, v3)]
Title:Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
View PDFAbstract:We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL). Learning a robust principal policy requires anticipating the worst possible strategic responses of other agents, which is generally NP-hard. However, we show that no-regret dynamics can identify these worst-case responses in poly-time in smooth games. We propose a framework that uses this policy evaluation method for efficiently learning a robust principal policy using RL. This framework can be extended to provide robustness to boundedly rational agents too. Our motivating application is automated mechanism design: we empirically demonstrate our framework learns robust mechanisms in both matrix games and complex spatiotemporal games. In particular, we learn a dynamic tax policy that improves the welfare of a simulated trade-and-barter economy by 15%, even when facing previously unseen boundedly rational RL taxpayers.
Submission history
From: Eric Zhao [view email][v1] Thu, 10 Jun 2021 04:32:20 UTC (2,790 KB)
[v2] Sat, 16 Apr 2022 05:23:13 UTC (1,265 KB)
[v3] Tue, 20 Dec 2022 03:33:34 UTC (1,254 KB)
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