139 research outputs found
A General Framework for Learning Mean-Field Games
This paper presents a general mean-field game (GMFG) framework for
simultaneous learning and decision-making in stochastic games with a large
population. It first establishes the existence of a unique Nash Equilibrium to
this GMFG, and demonstrates that naively combining reinforcement learning with
the fixed-point approach in classical MFGs yields unstable algorithms. It then
proposes value-based and policy-based reinforcement learning algorithms (GMF-V
and GMF-P, respectively) with smoothed policies, with analysis of their
convergence properties and computational complexities. Experiments on an
equilibrium product pricing problem demonstrate that GMF-V-Q and GMF-P-TRPO,
two specific instantiations of GMF-V and GMF-P, respectively, with Q-learning
and TRPO, are both efficient and robust in the GMFG setting. Moreover, their
performance is superior in convergence speed, accuracy, and stability when
compared with existing algorithms for multi-agent reinforcement learning in the
-player setting.Comment: 43 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:1901.0958
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