  Reinforcement learning, which acquires a policy maximizing long-term rewards,
has been actively studied. Unfortunately, this learning type is too slow and
difficult to use in practical situations because the state-action space becomes
huge in real environments. Many studies have incorporated human knowledge into
reinforcement Learning. Though human knowledge on trajectories is often used, a
human could be asked to control an AI agent, which can be difficult. Knowledge
on subgoals may lessen this requirement because humans need only to consider a
few representative states on an optimal trajectory in their minds. The
essential factor for learning efficiency is rewards. Potential-based reward
shaping is a basic method for enriching rewards. However, it is often difficult
to incorporate subgoals for accelerating learning over potential-based reward
shaping. This is because the appropriate potentials are not intuitive for
humans. We extend potential-based reward shaping and propose a subgoal-based
reward shaping. The method makes it easier for human trainers to share their
knowledge of subgoals. To evaluate our method, we obtained a subgoal series
from participants and conducted experiments in three domains,
four-rooms(discrete states and discrete actions), pinball(continuous and
discrete), and picking(both continuous). We compared our method with a baseline
reinforcement learning algorithm and other subgoal-based methods, including
random subgoal and naive subgoal-based reward shaping. As a result, we found
out that our reward shaping outperformed all other methods in learning
efficiency.
