Skip to yearly menu bar Skip to main content


Contributed Talk
in
Workshop: Learning by Instruction

Teaching Multiple Tasks to an RL Agent using LTL

Rodrigo Toro Icarte · Sheila McIlraith


Abstract:

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 off-policy RL to speed up learning without compromising convergence guarantees, and show that our method outperforms the state-of-the-art.

Live content is unavailable. Log in and register to view live content