A memory-based reinforcement learning model utilizing macro-actions

One of the difficulties in reinforcement learning (RL) is that an optimal policy is acquired through enormous trials. As a solution to reduce waste explorations in learning, recently the exploitation of macro-actions has been focused. In this paper, we propose a memory-based reinforcement learning model in which macro-actions are generated and exploited effectively. Through the experiments for two standard tasks, we confirmed that our proposed method could decrease waste explorations especially in the early training stage. This property contributes to enhancing training efficiency in RL tasks.