In this paper a reinforcement fuzzy learning scheme for robots playing a differential game is derived. A differential game may be considered a Markov decision process in continuous time, with continuous states and actions. The robots receive reinforcements from the environment after they take an action; and this reinforcement is then used to adapt a fuzzy controller that stores the experience accumulated by the robot. Every calculation is done in a physical system based on microcontrollers to control the movement of the robots and sensors to measure their position and angle in a 2D-plane. Filters are also implemented to approximate the derivatives of the states. Experiments of a pursuer-evader game are provided in order to show the feasibility of the technique. It should be noted, though, that the technique may also be used in a multi-game environment.

Differential games, Intelligent systems, Learning, Pursuer-evader games
2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Department of Systems and Computer Engineering

Givigi Jr., S.N. (Sidney N.), Schwartz, H.M, & Lu, X. (Xiaosong). (2009). An experimental adaptive fuzzy controller for differential games. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 3017–3023). doi:10.1109/ICSMC.2009.5345932