  Providing reinforcement learning agents with informationally rich human
knowledge can dramatically improve various aspects of learning. Prior work has
developed different kinds of shaping methods that enable agents to learn
efficiently in complex environments. All these methods, however, tailor human
guidance to agents in specialized shaping procedures, thus embodying various
characteristics and advantages in different domains. In this paper, we
investigate the interplay between different shaping methods for more robust
learning performance. We propose an adaptive shaping algorithm which is capable
of learning the most suitable shaping method in an on-line manner. Results in
two classic domains verify its effectiveness from both simulated and real human
studies, shedding some light on the role and impact of human factors in
human-robot collaborative learning.
