Examination of skill-based learning by inverse reinforcement learning using evolutionary process

In this paper, we propose skill-based learning by inverse reinforcement learning using evolutionary process. Reinforcement learning requires a large amount of time and learning convergence does not depend on the learning targets. In addition, if the learning targets are not known clearly, the appropriate reward cannot be defined and this makes learning difficult. Sub-goal method and inverse reinforcement learning are effective for each problem. They can deal with the problem that it requires a large amount of time and finding appropriate reward is difficult. However, in case that there is interference between behavior rules, the learning is not achieved efficiently by the sub-goal method. Therefore, in this study, the process of learning each behavior rules simultaneously is made with evolutionary process and reward functions for the half way are obtained by inverse reinforcement learning of the process. The target behavior is achieved by using the reward functions. This proposed method is called skill-based learning. Finally, effectiveness of skill-based learning is confirmed by experiment of driving task.