The University of Electro-Communications
Research Fellow of Japan Society for the Promotion of Science (DC1)
Abstract of my research
My research discusses important factors for zero communication, multi-agent cooperation by modifying reinforcement learning methods. I already proposed the two learning methods. The first method is called Profit Minimizing Reinforcement Learning (PMRL); it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL); it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. The simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change.
“Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem,”
of Control, Measurement, and System Integration, Vol. 10, pp.258-267, 2017
“Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation,”
Journal of Advanced Computational Intelligence and Intelligent Informatics Vol.21 No.5 p. 917-929Download »
“Recovery System Based on Exploration-biased Genetic Algorithm for Stuck Rover in Planetary Exploration,”
Journal of Robotics and Mechatronics Vol.29 No.5
Hello. I’m Fumito Uwano from the University of Electro-Communications.
I belongs to this laboratory in order to study many methods for Artificial Intelligence. Especially, I interested in multi-agent reinforcement learning.