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Poster
State Aware Imitation Learning
Yannick Schroecker · Charles Isbell

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #8
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Imitation learning is the study of learning how to act given a set of demonstrations provided by a human expert. It is intuitively apparent that learning to take optimal actions is a simpler undertaking in situations that are similar to the ones shown by the teacher. However, imitation learning approaches do not tend to use this insight directly. In this paper, we introduce State Aware Imitation Learning (SAIL), an imitation learning algorithm that allows an agent to learn how to remain in states where it can confidently take the correct action and how to recover if it is lead astray. Key to this algorithm is a gradient learned using a temporal difference update rule which leads the agent to prefer states similar to the demonstrated states. We show that estimating a linear approximation of this gradient yields similar theoretical guarantees to online temporal difference learning approaches and empirically show that SAIL can effectively be used for imitation learning in continuous domains with non-linear function approximators used for both the policy representation and the gradient estimate.

Keywords: Reinforcement Learning and Planning · Reinforcement Learning
[ Paper] 

Author Information

Yannick Schroecker (Georgia Institute of Technology)
Charles Isbell (Georgia Tech)
Charles Isbell

Dr. Charles Isbell received his bachelor's in Information and Computer Science from Georgia Tech, and his MS and PhD at MIT's AI Lab. Upon graduation, he worked at AT&T Labs/Research until 2002, when he returned to Georgia Tech to join the faculty as an Assistant Professor. He has served many roles since returning and is now The John P. Imlay Jr. Dean of the College of Computing. Charles’s research interests are varied but the unifying theme of his work has been using machine learning to build autonomous agents who engage directly with humans. His work has been featured in the popular press, congressional testimony, and in several technical collections. In parallel, Charles has also pursued reform in computing education. He was a chief architect of Threads, Georgia Tech’s structuring principle for computing curricula. Charles was also an architect for Georgia Tech’s First-of-its’s-kind MOOC-supported MS in Computer Science. Both efforts have received international attention, and been presented in the academic and popular press. In all his roles, he has continued to focus on issues of broadening participation in computing, and is the founding Executive Director for the Constellations Center for Equity in Computing. He is an AAAI Fellow and a Fellow of the ACM. Appropriately, his citation for ACM Fellow reads “for contributions to interactive machine learning; and for contributions to increasing access and diversity in computing”.

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