Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

Keuntaek Lee; Bogdan Vlahov; Jason Gibson; James M. Rehg; Evangelos A. Theodorou

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arXiv · 2004.08051

Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

Keuntaek Lee·Bogdan Vlahov·Jason Gibson·James M. Rehg·Evangelos A. Theodorou

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Abstract

In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments. The proposed process allows for simple training and robustness to out-of-sample data. We apply our method to the task of vision-based autonomous driving in multiple real and simulated environments and show its generalizability.

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BibTeX RIS

Keuntaek Lee, Bogdan Vlahov, Jason Gibson, James M. Rehg, Evangelos A. Theodorou. 2020-04-17. Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning. https://arxiv.org/abs/2004.08051

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