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.
A formal method's approach to skill composition in reinforcement learning tasks The paper combines RL and constraints expressed by logical formulas by setting up an automation from scTLTL formulas. Proposes a method that helps to construct policy from learned subtasks on the topic of combining RL tasks with linear temporal logic formulas.