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Exploring Long-Horizon Reasoning with Deep RL in Combinatorially Hard Tasks
Andrew C Li · Pashootan Vaezipoor · Rodrigo A Toro Icarte · Sheila McIlraith

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in Decision Awareness in Reinforcement Learning »
Event URL: https://openreview.net/forum?id=7vPSZASOF0o »

Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is less often observed in long-horizon, continuous domains with high-dimensional observations, where instead RL research has predominantly focused on problems with simple high-level structure (e.g. opening a drawer or moving a robot as fast as possible). Inspired by combinatorially hard optimization problems, we propose a set of robotics tasks which admit many distinct solutions at the high-level, but require reasoning about states and rewards thousands of steps into the future for the best performance. Critically, while RL has traditionally suffered on complex, long-horizon tasks due to sparse rewards, our tasks are carefully designed to be solvable without specialized exploration. Nevertheless, our investigation finds that standard RL methods often neglect long-term effects due to discounting, while general-purpose hierarchical RL approaches struggle unless additional abstract domain knowledge can be exploited.

Author Information

Andrew C Li (University of Toronto and Vector Institute)
Pashootan Vaezipoor (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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