Coordination of multiple behaviors acquired by a vision-based reinforcement learning
نویسندگان
چکیده
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 di erent action-value functions, switching action-value functions according to situations, and learning with previously obtained actionvalue 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. Three kinds of coordinations are compared with each other by computer simulations and our on-going real experiments are explained.
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