Skip to yearly menu bar Skip to main content


Meta-learning Parameterized Skills

Haotian Fu · Shangqun Yu · Saket Tiwari · Michael L. Littman · George Konidaris

Exhibit Hall 1 #718
[ ]
[ PDF [ Poster


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 highly difficult long-horizon (obstacle-course and robot manipulation) tasks.

Chat is not available.