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• W3014321323 abstract "In this paper, we consider a finite horizon, non-stationary, mean field games (MFG) with a large population of homogeneous players, sequentially making strategic decisions, where each player is affected by other players through an aggregate population state termed as mean field state. Each player has a private type that only it can observe, and a mean field population state representing the empirical distribution of other players' types, which is shared among all of them. Recently, authors in [1] provided a sequential decomposition algorithm to compute mean field equilibrium (MFE) for such games which allows for the computation of equilibrium policies for them in linear time than exponential, as before. In this paper, we extend it for the case when state transitions are not known, to propose a reinforcement learning algorithm based on Expected Sarsa with a policy gradient approach that learns the MFE policy by learning the dynamics of the game simultaneously. We illustrate our results using cyber-physical security example." @default.
• W3014321323 created "2020-04-10" @default.
• W3014321323 creator A5035299142 @default.
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• W3014321323 date "2020-04-05" @default.
• W3014321323 modified "2023-09-27" @default.
• W3014321323 title "Model-free Reinforcement Learning for Non-stationary Mean Field Games" @default.
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• W3014321323 hasPublicationYear "2020" @default.
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