Continuous-time path planning for multi-agents with fuzzy reinforcement learning

Abstract

There are a lot of applications of multi-agent systems, such as robot navigation, distributed control, data mining, etc. Reinforcement learning (RL) is a popular method used in multi agent path planning. RL algorithm needs an accurate representation of a small and discrete space. In order to plan multi agents in continuous time, this paper approximate the Q-values with the fuzzy logic, such that the modified RL can work in continuous state space. The fuzzy reinforcement learning proposed in this paper uses fuzzy Q-iteration algorithm and a modified Wolf-PH algorithm. The convergence and existence of the algorithm are proven. The continuous time planning algorithm is applied to a cooperative task of two mobile Khepera robots. The experimental results show the effectiveness of the new path planning method for the multi agents in continuous time.