Automata Guided Skill Composition
Authors: Xiao Li, Yao Ma, Calin Belta
Keywords: Skill composition, temporal logic, finite state automata
TL;DR: A formal method's approach to skill composition in reinforcement learning tasks
Abstract: Skills learned through (deep) reinforcement learning often generalizes poorly
across tasks and re-training is necessary when presented with a new task. We
present a framework that combines techniques in formal methods with reinforcement
learning (RL) that allows for the convenient specification of complex temporal
dependent tasks with logical expressions and construction of new skills from existing
ones with no additional exploration. We provide theoretical results for our
composition technique and evaluate on a simple grid world simulation as well as
a robotic manipulation task.