Kentarou Kurashige

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Various studies related to machine learning have been performed. In this study, we focus on reinforcement learning, one of the methods used in machine learning. In conventional reinforcement leaning, the design of the reward function is difficult, because it is a complex and laborious task and requires expert knowledge. In previous studies, the robot(More)
Various learning methods are adapted for experimental robot. We can make movement of a robot by giving teaching signals to a robot. But it is heavy for operator to define how to give teaching signals generally because operator must guess and think of a task and environment and define a function to do that. Here the author aim to create teaching signals(More)
Reinforcement learning (RL) is one of the methods for robot action learning. RL is formulated as the maximization of a single reward; however, in most practical problems, multiple objectives need to be considered. Therefore, it is necessary to perform multi-objective optimization. We focus on the required objectives that depended on the state of the robot(More)
Various studies related to machine learning have been performed. In this study, we focus on reinforcement learning, which is one of the methods used in machine learning. In conventional reinforcement leaning, the reward function is difficult to design, because it is complex and laborious and it requires expert knowledge. In previous studies, the robot(More)
Much research has been conducted on the application of reinforcement learning to robots. Learning time is a matter of concern in reinforcement learning. In reinforcement learning, information from sensors is projected on to a state space. A robot learns the correspondence between each state and action in state space and determines the best correspondence.(More)