Learning Fuzzy Logic Controller For Reactive Robot Behaviours

Fuzzy logic plays an important role in the design of reactive robot behaviours. This paper presents a learning approach to the development of a fuzzy logic controller, based on the delayed rewards from the real world. The delayed rewards are apportioned to the individual fuzzy rules by using reinforcement Q-learning. The efficient exploration of a solution space is one of the key issues in the reinforcement learning. A specific genetic algorithm is developed in this paper to trade off the exploration of learning spaces and the exploitation of learned experience. The proposed approach is evaluated on some reactive behaviour of the football-playing robotsc.