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arXiv · 1406.5979

Reinforcement and Imitation Learning via Interactive No-Regret Learning

Stephane Ross·J. Andrew Bagnell
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Abstract

Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learning, however, neither require nor benefit from information about the cost of actions. We extend existing results in two directions: first, we develop an interactive imitation learning approach that leverages cost information; second, we extend the technique to address reinforcement learning. The results provide theoretical support to the commonly observed successes of online approximate policy iteration. Our approach suggests a broad new family of algorithms and provides a unifying view of existing techniques for imitation and reinforcement learning.

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Stephane Ross, J. Andrew Bagnell. 2014-06-23. Reinforcement and Imitation Learning via Interactive No-Regret Learning. https://arxiv.org/abs/1406.5979

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