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- W2214884926 abstract "This work proposes a novel Q-learning algorithm to solve the problem of non-zero sum Nash games of linear time invariant systems with N-players (control inputs) and centralized uncertain/unknown dynamics. We first formulate the Q-function of each player as a parametrization of the state and all other the control inputs or players. An integral reinforcement learning approach is used to develop a model-free structure of N-actors/N-critics to estimate the parameters of the N-coupled Q-functions online while also guaranteeing closed-loop stability and convergence of the control policies to a Nash equilibrium. A 4th order, simulation example with five players is presented to show the efficacy of the proposed approach." @default.
- W2214884926 created "2016-06-24" @default.
- W2214884926 creator A5040301558 @default.
- W2214884926 date "2015-11-01" @default.
- W2214884926 modified "2023-10-14" @default.
- W2214884926 title "Non-zero sum Nash Q-learning for unknown deterministic continuous-time linear systems" @default.
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- W2214884926 doi "https://doi.org/10.1016/j.automatica.2015.08.017" @default.
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