Before revision:  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. Importantly , the generator is learned to directly maximize the down-stream planning performance.
After revision:  We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning , the generator is learned to directly maximize the down-stream planning performance.