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Poster
Distributionally Robust Imitation Learning
Mohammad Ali Bashiri · Brian Ziebart · Xinhua Zhang

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @
in Poster Session 8 »

We consider the imitation learning problem of learning a policy in a Markov Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available. Although the goal of imitation learning is to learn a policy that produces behaviors nearly as good as the experts’ for a desired task, assumptions of consistent optimality for demonstrated behaviors are often violated in practice. Finding a policy that is distributionally robust against noisy demonstrations based on an adversarial construction potentially solves this problem by avoiding optimistic generalizations of the demonstrated data. This paper studies Distributionally Robust Imitation Learning (DRoIL) and establishes a close connection between DRoIL and Maximum Entropy Inverse Reinforcement Learning. We show that DRoIL can be seen as a framework that maximizes a generalized concept of entropy. We develop a novel approach to transform the objective function into a convex optimization problem over a polynomial number of variables for a class of loss functions that are additive over state and action spaces. Our approach lets us optimize both stationary and non-stationary policies and, unlike prevalent previous methods, it does not require repeatedly solving an inner reinforcement learning problem. We experimentally show the significant benefits of DRoIL’s new optimization method on synthetic data and a highway driving environment.

Keywords: Optimization · Reinforcement Learning and Planning

Author Information

Mohammad Ali Bashiri (University of Illinois at Chicago)
Brian Ziebart (University of Illinois at Chicago)
Xinhua Zhang (University of Illinois at Chicago (UIC))

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