Title: An Experimental Study for Exploration-oriented Behavior in Maze-solving using Reinforcement Learning

Year of Publication: Aug - 2019
Page Numbers: 47-53
Authors: Masashi SUGIMOTO, Shinji TSUZUKI, Shiro URUSHIHARA, Kazunori HOSOTANI, Manabu KATO, Hitoshi SORI, Hiroyuki INOUE
Conference Name: The Fifth International Conference on Electronics and Software Science (ICESS2019)
- Japan


In this study, the reinforcement learning agent under the situation of communicable as multi-agent system will be improved efficiency. In reinforcement learning, this method will be supposed that agent is able to observe the environment, completely. However, there is a limit on the information of the sensors. Moreover, it is hard to learn the reinforcement learning agent in the actual environment cause of some noise of actual environment or source device. In addition, a time per a episode will enlarge because an agent will be explored in a given area. In this study, the proposed method has been using two type agents that communicate as information exchange on the location to settle this problem, moreover, the noise will be mixed with knowledge space in the situation of the knowledge sharing. In addition, sometimes the any information won’t be transmitted in the situation of knowledge sharing. From this viewpoint, in this study aims to improve maze-solving technique, efficiency by which to the multi-agent reinforcement learning’s agents under the situation. As a result, the proposed method has been confirmed that is provided suitable solution for an approach to the goal for the agents.