Teaching Multiple Tasks to an RL Agent using LTL

This paper examines the problem of how to teach multiple tasks to a Reinforcement Learning (RL) agent. To this end, we use Linear Temporal Logic (LTL) as a language for specifying multiple tasks in a manner that supports the composition of learned skills. We also propose a novel algorithm that exploits LTL progression and offpolicy RL to speed up learning without compromising convergence guarantees, and show that our method outperforms the state-ofthe-art approach on randomly generated Minecraft-like grids.