Integrating reinforcement learning with human demonstrations of varying ability

This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity, as well as the effect of combining demonstrations from multiple teachers. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration. The best performance was achieved by combining the best demonstrations from two teachers.