Time and place: Thursday, May 13, 12:10-12:40, 202 Chernoff Hall Title: Stochastic Adaptive Nash Certainty Equivalence Control with Population Parameter Estimation (½ hour) Abstract: For noncooperative games the Nash Certainty Equivalence (NCE), or Mean Field (MF) methodology developed in [1,2,3,4] provides decentralized strategies which asymptotically yield Nash equilibria. The NCE (MF) control laws use only the local information of each agent on its own state evolution and knowledge of its own dynamical parameters, while the behaviour of the mass is precomputable from knowledge of the distribution of dynamical and cost function parameters throughout the mass population. Relaxing the a priori information condition introduces the methods of parameter estimation and stochastic adaptive control (SAC) into MF control theory. A problem on this path is that where each agent needs to estimate (i) its own dynamical parameters, (ii) the distribution of the population's dynamical parameters [5], (iii) the distribution of the population's cost function parameters [6]. In the present work, each agent observes a random subset of the population of agents. Each agent estimates its own dynamical parameters via the recursive weighted least squares (RWLS) algorithm, and the distribution of the population's dynamical and cost function parameters via the maximum likelihood estimation (MLE). Under reasonable conditions, we establish: (i) the strong consistency of the self-parameter estimates and the weak convergence of the distribution functions; and that (ii) all agent systems are long run average L^{2} stable; (iii) the set of controls yields a (strong) e-Nash equilibrium for all e; and (iv) in the population limit the long run average cost obtained is equal to the non-adaptive long run average cost.
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