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Coordination of multiple behaviors acquired by a vision-based reinforcement learning

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5 Author(s)
Asada, M. ; Dept. of Mech. Eng. for Comput.-Controlled Machinery, Osaka Univ., Japan ; Uchibe, E. ; Noda, S. ; Tawaratsumida, S.
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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 represented by an action-value function in terms of state of the environment and robot action. Next, three kinds of coordinations of multiple behaviors are considered; simple summation of different action-value functions, switching action-value functions according to situations, and learning with previously obtained action-value functions as initial values of a new action-value function. A task of shooting a ball into the goal avoiding collisions with an enemy is examined. The task can be decomposed into a ball shooting subtask and a collision avoiding subtask. These subtasks should be accomplished simultaneously, but they are not independent of each other

Published in:

Intelligent Robots and Systems '94. 'Advanced Robotic Systems and the Real World', IROS '94. Proceedings of the IEEE/RSJ/GI International Conference on  (Volume:2 )

Date of Conference:

12-16 Sep 1994