Hierarchical Actor-Critic

12/04/2017

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by   Andrew Levy, et al.

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We present a novel approach to hierarchical reinforcement learning called Hierarchical Actor-Critic (HAC). HAC aims to make learning tasks with sparse binary rewards more efficient by enabling agents to learn how to break down tasks from scratch. The technique uses of a set of actor-critic networks that learn to decompose tasks into a hierarchy of subgoals. We demonstrate that HAC significantly improves sample efficiency in a series of tasks that involve sparse binary rewards and require behavior over a long time horizon.