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Oral
Hierarchical Imitation and Reinforcement Learning
Hoang Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daumé III

Fri Jul 13 02:00 AM -- 02:20 AM (PDT) @ A1
in Reinforcement Learning 15 »

We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.

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Author Information

Hoang Le (Caltech)

Hoang M. Le is a PhD Candidate in the Computing and Mathematical Sciences Department at the California Institute of Technology. He received a M.S. in Cognitive Systems and Interactive Media from the Universitat Pompeu Fabra, Barcelona, Spain, and a B.A. in Mathematics from Bucknell University in Lewisburg, PA. He is a recipient of an Amazon AI Fellowship. Hoang’s research focuses on the theory and applications of sequential decision making, with a strong focus on imitation learning. He has broad familiarity with the latest advances in imitation learning techniques and applications. His own research in imitation learning blends principled new techniques with a diverse range of application domains. In addition to popular reinforcement learning domains such as maze navigation and Atari games, his prior work on imitation learning has been applied to learning human behavior in team sports and developing automatic camera broadcasting system.

Nan Jiang (Microsoft Research)
Alekh Agarwal (Microsoft Research)
Miroslav Dudik (Microsoft Research)
Miroslav Dudik

Miroslav Dudík is a Senior Principal Researcher in machine learning at Microsoft Research, NYC. His research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization, and algorithms. Most recently he has worked on contextual bandits, reinforcement learning, and algorithmic fairness. He received his PhD from Princeton in 2007. He is a co-creator of the Fairlearn toolkit for assessing and improving the fairness of machine learning models and of the Maxent package for modeling species distributions, which is used by biologists around the world to design national parks, model the impacts of climate change, and discover new species.

Yisong Yue (Caltech)
Yisong Yue

Yisong Yue is a Professor of Computing and Mathematical Sciences at Caltech and (via sabbatical) a Principal Scientist at Latitude AI. His research interests span both fundamental and applied pursuits, from novel learning-theoretic frameworks all the way to deep learning deployed in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Latitude AI, he is working on machine learning approaches to motion planning for autonomous driving.

Hal Daumé III (Microsoft Research)

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