Data-Efficient Hierarchical Reinforcement Learning

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how w...More

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