A Neuro-fuzzy Network with Reinforcement Learning Algorithms for Swarm Learning

An internal model of autonomous mobile robots (agent) is proposed in this paper. A TSK-type fuzzy net is used as a classifier of environment information, i.e., the state of an agent, and reinforcement learning methods such as Q-learning, sarsa-learning are used to make multiple agents acquire adaptive behaviors. Goal navigated exploration problem was simulated to confirm the effectiveness of the proposed methods, and the results showed that the new learning methods are more efficient than actor-critic method which was proposed by our previous work.