This paper mainly discusses learning in pursuit-evasion game. In the pursuit-evasion model, one robot pursues another one in a partially known environment. Partially known environment means that each robot knows the instant position of the other robot but at the same time none of them knows its control strategy. Therefore, both robots have to self-learn their control strategies on-line by interaction with each other. A new hybrid learning technique is proposed. The proposed technique combines reinforcement learning with both a fuzzy controller and genetic algorithms in a two-phase structure. The proposed technique is called a Q(λ)-learning based genetic fuzzy controller (QLBGFC). To test the performance of our proposed technique, it is compared with the optimal strategy, the Q(λ)-learning, and the reward-based genetic algorithms. Computer simulations show the usefulness of the proposed technique. In addition, the convergence and the boundedness of the Q-learning algorithm used in the proposed technique are shown.

Fuzzy control, Genetic algorithms, Hybrid learning, Multi-robot system, Pursuit-evasion game, Q(λ)-learning, Reinforcement learning
dx.doi.org/10.1109/ICSMC.2009.5346111
2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Department of Systems and Computer Engineering

Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2009). A novel hybrid learning technique applied to a self-learning multi-robot system. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2616–2623). doi:10.1109/ICSMC.2009.5346111