# Burn-In Demonstrations for Multi-Modal Imitation Learning

arXiv: https://arxiv.org/abs/1710.05090 · HTML: https://mlsanity.com/paper/1710.05090/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling](https://mlsanity.com/paper/2408.17355/) (2024)
- [Meta-Inverse Reinforcement Learning with Probabilistic Context Variables](https://mlsanity.com/paper/1909.09314/) (2019)
- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [Learning a Prior over Intent via Meta-Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.12573/) (2018)
- [Sample-Efficient Imitation Learning via Generative Adversarial Nets](https://mlsanity.com/paper/1809.02064/) (2018)
- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://mlsanity.com/paper/2210.08073/) (2022)
- [Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets](https://mlsanity.com/paper/2005.10622/) (2020)
- [Good Data Is All Imitation Learning Needs](https://mlsanity.com/paper/2409.17605/) (2024)

## Abstract (arXiv)

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time. Our approach involves reformulating the typical imitation learning setting to include "burn-in demonstrations" upon which policies are conditioned at test time. We demonstrate that our approach outperforms standard InfoGAIL in maximizing the mutual information between predicted and unseen style labels in road scene simulations, and we show that our method leads to policies that imitate expert autonomous driving systems over long time horizons.