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Fast Inference and Transfer of Compositional Task for Few-shot Task Generalization
Sungryull Sohn · Hyunjae Woo · Jongwook Choi · Izzeddin Gur · Aleksandra Faust · Honglak Lee

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in Deep Reinforcement Learning »
Event URL: https://openreview.net/forum?id=e89keF9vw9Q »

We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph that defines a set of subtasks and their dependencies that are unknown to the agent. Different from the previous meta-rl methods trying to directly infer the unstructured task embedding, our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks, and use it as a prior to improve the task inference in testing. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks than various existing algorithms such as meta reinforcement learning, hierarchical reinforcement learning, and other heuristic agents.

Author Information

Sungryull Sohn (LG AI research US)
Hyunjae Woo (University of Michigan)
Jongwook Choi (University of Michigan)
Izzeddin Gur (Google)
Aleksandra Faust (Google Brain)

Aleksandra Faust is a Senior Research Engineer at Google Brain, specializing in robot intelligence. Previously, Aleksandra led machine learning efforts for self-driving car planning and controls in Waymo and Google X, and was a researcher in Sandia National Laboratories, where she worked on satellites and other remote sensing applications. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), a Master’s in Computer Science from University of Illinois at Urbana-Champaign, and a Bachelor’s in Mathematics from University of Belgrade, Serbia. Her research interests include reinforcement learning, adaptive motion planning, and machine learning for decision-making. Aleksandra won Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in Engineering, Mathematics, and Sciences in the period of 2011-2014. She was also awarded with the Best Paper in Service Robotics at ICRA 2018, Sandia National Laboratories’ Doctoral Studies Program and New Mexico Space Grant fellowships, as well as the Outstanding Graduate Student in Computer Science award. Her work has been featured in the New York Times.​

Honglak Lee (U. Michigan)

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