Imitation with Neural Density Models

• URL: http://arxiv.org/abs/2010.09808v1
• Date: Mon, 19 Oct 2020 19:38:36 GMT
• Title: Imitation with Neural Density Models
• Authors: Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
• Abstract summary: We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Imitation 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.
• Score: 98.34503611309256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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