The Logical Options Framework

Brandon Araki · Xiao Li · Kiran Vodrahalli · Jonathan DeCastro · Micah Fry · Daniela Rus


Keywords: [ Planning and Control ] [ Reinforcement Learning and Planning ]

[ Abstract ]
[ Slides
[ Paper ]
[ Visit Poster at Spot C5 in Virtual World ]
Wed 21 Jul 9 a.m. PDT — 11 a.m. PDT
Oral presentation: Reinforcement Learning 13
Wed 21 Jul 7 a.m. PDT — 8 a.m. PDT


Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF's learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps on our benchmarks. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.

Chat is not available.