Multi-Model Adaptive Learning For Robots Under Uncertainty

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. A novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players' strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. In contrast, to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters of the algorithm for a specific problem a priori. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.