title: Automata Guided Skill Composition
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.
authors: Xiao Li Yao Ma Calin Belta
hIndex: Calin Belta 50