Shoichi Noda

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This paper presents a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal. We discuss several issues in applying the reinforcement learning method to a real robot with vision sensor by which the robot can obtain information about the changes in an environment. First, we construct a state space in terms of size,(More)
A method is proposed which accomplishes a whole task consisting of plural subtasks by coordinating multiple behaviors acquired by a vision-based reinforcement learning. First, individual behaviors which achieve the corresponding subtasks are independently acquired by Q-learning, a widely used reinforcement learning method. Each learned behavior can be(More)
We propose a method which acquires a purposive behavior for a mobile robot to shoot a ball into the goal by using a vision-based reinforcement learning. A mobile robot (an agent) does not need to know any parameters of the 3-D environment or its kinematics/dynamics. Information about the changes of the environment is only the image captured from a single TV(More)
This paper presents a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal, and discusses several issues in applying the reinforcement learning method to a real robot with vision sensor. First, a \state-action deviation" problem is found as a form of perceptual aliasing in constructing the state and action spaces(More)
A clinicopathological study of gastric carcinoma was carried out according to Lauren's classification, on 207 patients, who underwent gastric resection at Department of Surgery II, Kyushu University, Faculty of Medicine from 1970 to 1972. Among them 93 cases (44.9%) were of intestinal-type carcinoma, 71 (34.3%) of diffuse carcinoma and 43 (20.8%) of other(More)
In [1], we have presented the soccer robot which had learned to shoot a ball into the goal using the Q-learning. In this paper, we discuss several issues in applying the Qlearning method to a real robot with vision sensor. First, to speed up the learning rate, we implement a mechanism of Learning form Easy Missions (or LEM) which is a similar technique to(More)