Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

international conference on learning representations, 2019.

Cited by: 34|Bibtex|Views146
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

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. Accordingly, the choice of representation -- the mapping of observation space to...More

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