Social navigation has been gaining attentions with the growth in machine
intelligence. Since reinforcement learning can select an action in the
prediction phase at a low computational cost, it has been formulated in a
social navigation tasks. However, reinforcement learning takes an enormous
number of iterations until acquiring a behavior policy in the learning phase.
This negatively affects the learning of robot behaviors in the real world. In
particular, social navigation includes humans who are unpredictable moving
obstacles in an environment. We proposed a reward shaping method with subgoals
to accelerate learning. The main part is an aggregation method that use
subgoals to shape a reinforcement learning algorithm. We performed a learning
experiment with a social navigation task in which a robot avoided collisions
and then reached its goal. The experimental results show that our method
improved the learning efficiency from a base algorithm in the task.