Advance Motion Acquisition of an Actual Robot by Reinforcement Learning using Reward Change

Abstract

Generally, a reward is given as the fixed value for the Reinforcement Learning process. However, as for a human being's training process, the reward could be changed according to the improvement of the process. Therefore, it is very interesting to study the Q-Learning process under the reward change. In this paper, Q-Learning is applied to an actual robot in order for advance motion acquisition. Results show that the Q-Learning process changes according to the reward change. Moreover, this paper clarifies how the robot obtains its optimal motion form under the learning process.