Automatic generation of macro-actions using genetic algorithm for reinforcement learning

The main problem of reinforcement learning is that the learning converges slowly. As one of the solutions, McGovern (1997) proposed the "macro-action". However, a human expert needs to design macro-actions which adapt to an environment. In this paper, we propose a new method that enables one to generate the macro-actions which adapt to the environment automatically using the genetic algorithm.