Abstract
In order to realize an intelligent agent such as autonomous mobile robots, Reinforcement Learning is one of the necessary techniques in the behavior control system. However, applying the reinforcement learning to actual sized problem, the "curse of dimensionality" problem in partition of sensory states should be avoided maintaining computational efficiency. In multi-agent reinforcement learning such as the "pursuit problem". the problem is emerged owing to the number of collaborating allied agents and a prey agent, as well as the size of environments. We proposed a hierarchical modular reinforcement learning in order to deal with the dimensional problem and task decomposition. In this study, we focused on investigation of the learning performance of agent that represents the input states in relative coordinate system, considering application as mobile robots. We showed effectiveness of the proposed learning algorithm based on relative expressions with limited view through numerical experiments of the pursuit problem.
