Before revision:  We propose to use temporally-extended macro-actions  to cut down the effective planning horizon and thus the exponential factor of the complexity. We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.
After revision:  We propose to use temporally-extended macro-actions to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.