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Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Batuhan Yardim · Semih Cayci · Matthieu Geist · Niao He

Wed Jul 26 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #327
Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous $N$-player games. However, limiting applicability, existing theoretical results assume variations of a ``population generative model'', which allows arbitrary modifications of the population distribution by the learning algorithm. Moreover, learning algorithms typically work on abstract simulators with population instead of the $N$-player game. Instead, we show that $N$ agents running policy mirror ascent converge to the Nash equilibrium of the regularized game within $\widetilde{\mathcal{O}}(\varepsilon^{-2})$ samples from a single sample trajectory without a population generative model, up to a standard $\mathcal{O}(\frac{1}{\sqrt{N}})$ error due to the mean field. Taking a divergent approach from the literature, instead of working with the best-response map we first show that a policy mirror ascent map can be used to construct a contractive operator having the Nash equilibrium as its fixed point. We analyze single-path TD learning for $N$-agent games, proving sample complexity guarantees by only using a sample path from the $N$-agent simulator without a population generative model. Furthermore, we demonstrate that our methodology allows for independent learning by $N$ agents with finite sample guarantees.

Author Information

Batuhan Yardim (ETH Zurich)
Semih Cayci (Rheinisch Westfälische Technische Hochschule Aachen)
Matthieu Geist (Google)
Niao He (ETH Zurich)

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