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LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Pashootan Vaezipoor · Andrew C Li · Rodrigo A Toro Icarte · Sheila McIlraith

Tue Jul 20 07:45 AM -- 07:50 AM (PDT) @
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language – linear temporal logic (LTL) – and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments. Experiments on discrete and continuous domains target combinatorial task sets of up to $\sim10^{39}$ unique tasks and demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.

Author Information

Pashootan Vaezipoor (University of Toronto and Vector Institute)
Andrew C Li (University of Toronto and Vector Institute)
Rodrigo A Toro Icarte (University of Toronto and Vector Institute)

I am a PhD student in the knowledge representation group at the University of Toronto. I am also a member of the Canadian Artificial Intelligence Association and the Vector Institute. My supervisor is Sheila McIlraith. I did my undergrad in Computer Engineering and MSc in Computer Science at Pontificia Universidad Católica de Chile (PUC). My master's degree was co-supervised by Alvaro Soto and Jorge Baier. While I was at PUC, I instructed the undergraduate course "Introduction to Programming Languages."

Sheila McIlraith (University of Toronto and Vector Institute)

Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, a Canada CIFAR AI Chair (Vector Institute), and a Research Lead at the Schwartz Reisman Institute for Technology and Society. McIlraith's research is in the area of AI sequential decision making broadly construed, with a focus on human-compatible AI. McIlraith is a Fellow of the ACM and AAAI.

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