Hierarchical reinforcement learning using a modular fuzzy model for multi-agent problem

Reinforcement learning is a promising approach to realize intelligent agent such as autonomous mobile robots. In order to apply the reinforcement learning to actual sized problem, the "curse of dimensionality" problem in partition of sensory states should be avoided maintaining computational efficiency. The paper describes a hierarchical modular reinforcement learning that Profit Sharing learning algorithm is combined with Q-Learning reinforcement learning algorithm hierarchically in multi-agent pursuit environment. As the model structure for such the huge problem, we propose a modular fuzzy model extending SIRMs architecture. Through numerical experiments, we found that the proposed method has good convergence property of learning compared with the conventional algorithms.