Effective Integration of Imitation Learning and Reinforcement Learning by Generating Internal Reward

Abstract

This paper describes an integrative machine learning architecture of imitation learning and reinforcement learning. The learning architecture aims to help integration of the two learning process by generating internal rewards. After observing superiors, human learners usually start practicing through trial and error. Humans usually learn tasks through both imitation learning and reinforcement learning. Imitation learning and reinforcement learning should be harmonized as an effective and integrative learning system. A simple reinforcement learning requires a huge amount of trials and errors in an agent's learning phase. However, imitation learning can reduce the amount of time. Based on this idea, the composition of reinforcement learning and imitation learning is proposed as an integrative machine learning architecture. In this paper, an additional internal reward system, which is generated by the learner agent, is introduced to achieve this goal. The learning architecture is evaluated through an experiment and the effectiveness of the integration is examined.