As a newly rising intelligent learning mode

reinforcement learning is being applied more and more in a learning system with unknown environment model because of its simple learning mechanism and no need of knowledge of the system or sample data in advance. However

one of the problems of the reinforcement learning method is that its learning speed is too low to ensure the real-time system. Researchers have studied to speed up learning by improving learning algorithm and adopting intelligent exploration policy or applying the hierarchical reinforcement learning method

etc. However

how to describe the reinforcement function and how the reinforcement function affects the learning speed are seldom studied. In the existing reinforcement learning system

the model-free reinforcement function artificially defined is usually used. Its simple and rough expression is one of the causes of the low efficiency of learning. In this article

a new fuzzy model-based reinforcement function structure is presented. It is described according to the actual application in the conflict-free path planning problem of a cooperative multiple mobile robot system. In this system

the robot behaviors are divided into three basic kinds moving to the goal

avoiding obstacles and other robots. Then

the subfunctions reflecting these basic behaviors of robots are hierarchically and fuzzily modeled

and the final reinforcement function is expressed by the sum of fuzzy weighted sub-functions. The fuzzy model based reinforcement function has more accurate expression of the influence of each robot’s action on the environment. The simulation shows that using the fuzzy model based reinforcement functions in reinforcement learning algorithm can further speed up the convergence than using model-free reinforcement functions.