Entropy Regularization for Mean Field Games with Learning
成果类型：
Article; Early Access
署名作者：
Guo, Xin; Xu, Renyuan; Zariphopoulou, Thaleia
署名单位：
University of California System; University of California Berkeley; University of Southern California; University of Oxford; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Oxford
刊物名称：
MATHEMATICS OF OPERATIONS RESEARCH
ISSN/ISSBN：
0364-765X
DOI：
10.1287/moor.2021.1238
发表日期：
2022
关键词：
摘要：
Entropy regularization has been extensively adopted to improve the efficiency, the stability, and the convergence of algorithms in reinforcement learning. This paper analyzes both quantitatively and qualitatively the impact of entropy regularization for mean field games (MFGs) with learning in a finite time horizon. Our study provides a theoretical justification that entropy regularization yields time-dependent policies and, furthermore, helps stabilizing and accelerating convergence to the game equilibrium. In addition, this study leads to a policy-gradient algorithm with exploration in MFG. With this algorithm, agents are able to learn the optimal exploration scheduling, with stable and fast convergence to the game equilibrium.
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