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arXiv · 2206.03597

Meta-Learning Parameterized Skills

Haotian Fu·Shangqun Yu·Saket Tiwari·Michael Littman·George Konidaris
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Abstract

We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks. We propose to leverage off-policy Meta-RL combined with a trajectory-centric smoothness term to learn a set of parameterized skills. Our agent can use these learned skills to construct a three-level hierarchical framework that models a Temporally-extended Parameterized Action Markov Decision Process. We empirically demonstrate that the proposed algorithms enable an agent to solve a set of difficult long-horizon (obstacle-course and robot manipulation) tasks.

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Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris. 2022-06-07. Meta-Learning Parameterized Skills. https://arxiv.org/abs/2206.03597

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