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Poster
Hierarchical Skills for Efficient Exploration
Jonas Gehring · Gabriel Synnaeve · Andreas Krause · Nicolas Usunier

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @
in Poster Session 8 »

In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. In previous work on continuous control, the sensitivity of methods to this trade-off has not been addressed explicitly, as locomotion provides a suitable prior for navigation tasks, which have been of foremost interest. In this work, we analyze this trade-off for low-level policy pre-training with a new benchmark suite of diverse, sparse-reward tasks for bipedal robots. We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner. For utilization on downstream tasks, we present a three-layered hierarchical learning algorithm to automatically trade off between general and specific skills as required by the respective task. In our experiments, we show that our approach performs this trade-off effectively and achieves better results than current state-of-the-art methods for end-to-end hierarchical reinforcement learning and unsupervised skill discovery.

Keywords: Reinforcement Learning and Planning

Author Information

Jonas Gehring (Facebook AI Research)
Gabriel Synnaeve (Facebook)
Andreas Krause (ETH Zurich)
Nicolas Usunier (Facebook AI Research)

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