Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

ICLR 2019 Ofir NachumShixiang GuHonglak LeeSergey Levine

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach... (read more)

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