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Laboratory Overview	Reinforcement Learning and Multiagent system We do research on reinforcement learning and its application to multiagent systems. Reinforcement learning make agent's policies by intensifying and supressing action rules through rewards and penalties given to agent's try and error processes. A mutiagent system is a set of agents that interact with each other and applied to designing intelligent systems.
Laboratory Character	Mutiagent systems are drawing attention of researchers to design intelligent contorolling systems in many research fields. Reinforcement learning is very appropriate to make agents learn or adapt to new environments that change dynamically. It is used to make intelligent game programs in Chess, Go and Shogi.
Research Outputs	You can review research papers in the website below. https://shibaura.pure.elsevier.com/en/persons/harukazu-igarashi
Laboratory Research Field	RoboCup Computer Shogi Reinforcement Learning Multiagent System
Recent Research Topics	Multiagent learning Monte Carlo softmax search Velocity control of a car based on Fuzzy control and reinforcement learning
Affiliated Conferences	The Japanese Society for Artificial Intelligence Information Processing Society of Japan The Institute of Electronics Information and Communication Engineers
For Social Contributions to the World Sustainability	In future years, our research will contribute to system management of multiple robots and computers collaborating with each other. An example of its application is a scheduling system that controls automatic guided vehicles safely and effectively.
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