Meta-Learning Parameterized Skills

• URL: http://arxiv.org/abs/2206.03597v4
• Date: Wed, 19 Jul 2023 04:52:33 GMT
• Title: Meta-Learning Parameterized Skills
• Authors: Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris
• Abstract summary: We propose a novel skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space. We empirically demonstrate that the proposed algorithms enable an agent to solve a set of difficult long-horizon (obstacle-course and robot manipulation) tasks.
• Score: 12.845774297648738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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