This paper discusses learning in n-purser n-evader games. In a pursuit-evasion game, one player (the pursuer) pursues another one while the other (the evader) tries to escape. We assume that each player only knows the instantaneous position of the other players but at the same time none of them knows its control strategy nor the control strategy of the other players. Therefore, the players have to self-learn their control strategies on-line by interaction with each other. In this paper, we extend our previous work from learning in a single pursuit-evasion game [1] to learning in a multi-pursuit-evasion game. We use the Q(λ)-learning based genetic fuzzy controller technique (QLBGFC) proposed in [1]. The proposed technique combines reinforcement learning with both a fuzzy controller and genetic algorithms in a two-phase structure. In addition to the proposed QLBGFC, we construct a new Q-table that is responsible for learning the coupling process between the pursuers and the evaders. To test the performance of the proposed technique, it is compared with the optimal strategy of a single pursuit-evasion game. Computer simulations show the usefulness of the proposed technique.

Differential game, Fuzzy control, Genetic algorithms, Multi-robot, Pursuit-evasion game, Q(λ)-learning, Reinforcement learning
2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Department of Systems and Computer Engineering

Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2010). Learning in n-Pursuer n-Evader Differential Games. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 4069–4074). doi:10.1109/ICSMC.2010.5642186