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arXiv · 2010.09808

Imitation with Neural Density Models

Kuno Kim·Akshat Jindal·Yang Song·Jiaming Song·Yanan Sui·Stefano Ermon
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Abstract

We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward. Our approach maximizes a non-adversarial model-free RL objective that provably lower bounds reverse Kullback-Leibler divergence between occupancy measures of the expert and imitator. We present a practical IL algorithm, Neural Density Imitation (NDI), which obtains state-of-the-art demonstration efficiency on benchmark control tasks.

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BibTeXRIS

Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon. 2020-10-19. Imitation with Neural Density Models. https://arxiv.org/abs/2010.09808

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