![]() ![]() ![]() KEYWORDS Mobile Robot, Exploration-exploitation Ratio, Reinforcement Fearning, Maze Problem, Route Planning. As a result, the proposed method has been confirmed that is provided suitable solution for an approach to the goal for the agents. 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. Thus, the self-decision mechanism will be installed. In addition, sometimes the any information won't be transmitted in the situation of knowledge sharing. 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, a time per a episode will enlarge because an agent will be explored in a given area. Calculate the slope between the pair of given points in each box. holes among their branches and beat devious pathways through the maze of spines. ![]() BOLTZMANN DENSITY FUNCTION MAZE LEARNING Breathing maze router for analog. Often the whole southern slope of some barren mountain is seen thickly. Moreover, it is hard to learn the reinforcement learning agent in the actual environment cause of some noise of actual environment or source device. fitting problem with data having near infinite slope close to the origin. However, there is a limit on the information of the sensors. In reinforcement learning, this method will be supposed that agent is able to observe the environment, completely. Abstract : In this study, the reinforcement learning agent under the situation of communicable as multi-agent system will be improved efficiency. ![]()
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