Decision Method of Policy Reuse Ratio for Transfer Reinforcement Learning

Abstract

This paper presents decision method of policy reusing ratio of transfer learning in reinforcement learning based on gradient descent method. In recent years, learning robot system has been discussed for the actual applications. To reduce the learning time, transfer learning framework is proposed and the method is knowledge reusing mechanism. In particular, effectiveness of transfer evaluation method such as a transfer surface is proposed in reinforcement learning and adjustment method of transferring ratio is also proposed. However, decision of value of transferring ratio is depends on human intuition and experience. In this paper, automatic transferring ratio estimation method is proposed based on gradient descent method with random initial value and statistics in multiple estimation trials, further evaluation of proposed method in two different transfer surface.