Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning

Abstract

This paper proposes an automatic policy selection method using spreading activation theory based on psychological theory for transfer learning in reinforcement learning. Intel-ligent robot systems have recently been studied for practical applications such as home robot, communication robot, and warehouse robot. Learning algorithms are key to building useful robot systems important. For example, a robot can explore for optimal policy with trial and error using reinforcement learning. Moreover, transfer learning enables reuse of prior policy and is effective for environment adaptability. However, humans de-termine applicable methods in transfer learning. Policy selection method has been proposed for transfer learning in reinforcement learning using spreading activation model proposed in cognitive psychology. In this paper, novel activation function and spreading sequence is discussed for spreading policy selection method. Fur-ther computer simulations are used to examine the effectiveness of the proposed method for automatic policy selection in simplified shortest-path problem.