Abstract
Recently, applications of multiagent system are expected from the viewpoint of parallel and distributed processing of systems. Reinforcement learning attracts attention as an implementing method of the multiagent systems. However, there is a problem that the more the number of agents to deal with increases, the slower the speed of learning becomes. To solve this problem, we propose a new reinforcement learning that can learn quickly and reduce the amount of memory. It tries to increase learning efficiency of hunter games by paying attention to partial state of two agents among a large number of agents.
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