Adaptively Shaping Reinforcement Learning Agents Via Human Reward

The computational complexity of reinforcement learning algorithms increases exponentially with the size of the problem. An effective solution to this problem is to provide reinforcement learning agents with informationally rich human knowledge, so as to expedite the learning process. Various integration methods have been proposed to combine human reward with agent reward in reinforcement learning. However, the essential distinction of these combination methods and their respective advantages and disadvantages are still unclear. In this paper, we propose an adaptive learning algorithm that is capable of selecting the most suitable method from a portfolio of combination methods in an adaptive manner. We show empirically that our algorithm enables better learning performance under various conditions, compared to the approaches using one combination method alone. By analyzing different ways of integrating human knowledge into reinforcement learning, our work provides some important insights into understanding the role and impact of human factors in human-robot collaborative learning.