mlsanityData to 7 Oct 2026

Imitation with Neural Density Models

arXiv 2010.09808 ↗First posted 19 Oct 2020cs.LG cs.AI stat.MLcited by 8

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2022: 3; 2023: 2; 2024: 2; 2025: 1.

8 papers.

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  1. 2021Probability Density Estimation Based Imitation Learning
  2. 2020Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
  3. 2023Conditional Kernel Imitation Learning for Continuous State Environments
  4. 2021Imitation Learning by Reinforcement Learning
  5. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  6. 2020Energy-Based Imitation Learning
  7. 2021Imitation by Predicting Observations
  8. 2023Provably Efficient Adversarial Imitation Learning with Unknown Transitions