  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. The essential factor for learning efficiency is
rewards. Potential-based reward shaping is a basic method for enriching
rewards. This method is required to define a specific real-value function
called a potential function for every domain. It is often difficult to
represent the potential function directly. SARSA-RS learns the potential
function and acquires it. However, SARSA-RS can only be applied to the simple
environment. The bottleneck of this method is the aggregation of states to make
abstract states since it is almost impossible for designers to build an
aggregation function for all states. We propose a trajectory aggregation that
uses subgoal series. This method dynamically aggregates states in an episode
during trial and error with only the subgoal series and subgoal identification
function. It makes designer effort minimal and the application to environments
with high-dimensional observations possible. We obtained subgoal series from
participants for experiments. We conducted the 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, our reward
shaping outperformed all other methods in learning efficiency.
