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Poster
Imitation with Neural Density Models
Kuno Kim · Akshat Jindal · Yang Song · Jiaming Song · Yanan Sui · Stefano Ermon

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

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.

Keywords: Reinforcement Learning and Planning

Author Information

Kuno Kim (Stanford)
Akshat Jindal
Yang Song (Stanford University)
Jiaming Song (Stanford University)

I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.

Yanan Sui (California Institute of Technology)
Stefano Ermon (Stanford)

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