In this paper we develop a reinforcement fuzzy learning scheme for robots playing a differential game. Differential games are games played in continuous time, with continuous states and actions. Fuzzy controllers are used to approximate the calculation of future reinforcements of the game due to actions taken at a specific time. If an immediate reinforcement reward function is defined, we may use a fuzzy system to tell what is the predicted reinforcement in a specified time ahead. This reinforcement is then used to adapt a fuzzy controller that stores the experience accumulated by the player. Simulations of a modified two car game are provided in order to show the potentiality of the technique. Experiments are performed in order to validate the method. Finally, it should be noted that although the game used as an example involves only two players, the technique may also be used in a multi-game environment.

Additional Metadata
Keywords Differential games, Fuzzy control, Intelligent systems, Learning, Pursuer-evader games, Reinforcement learning
Persistent URL dx.doi.org/10.1007/s10846-009-9380-4
Journal Journal of Intelligent and Robotic Systems: Theory and Applications
Citation
Givigi Jr., S.N. (Sidney N.), Schwartz, H.M, & Lu, X. (Xiaosong). (2010). A reinforcement learning adaptive fuzzy controller for differential games. Journal of Intelligent and Robotic Systems: Theory and Applications, 59(1), 3–30. doi:10.1007/s10846-009-9380-4