# Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields

arXiv: https://arxiv.org/abs/2503.06611 · HTML: https://mlsanity.com/paper/2503.06611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 10 of 10)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [An Introduction to Deep Generative Modeling](https://mlsanity.com/paper/2103.05180/) (2021)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://mlsanity.com/paper/1805.07687/) (2018)
- [Inverse reinforcement learning for video games](https://mlsanity.com/paper/1810.10593/) (2018)
- [Towards Generalized Inverse Reinforcement Learning](https://mlsanity.com/paper/2402.07246/) (2024)
- [Automated Feature Selection for Inverse Reinforcement Learning](https://mlsanity.com/paper/2403.15079/) (2024)

## Built on by (most cited first, 1 of 1)

- [Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models](https://mlsanity.com/paper/2604.02438/) (2026)

## Abstract (arXiv)

Performance and reliability analyses of autonomous vehicles (AVs) can benefit from tools that ``amplify'' small datasets to synthesize larger volumes of plausible samples of the AV's behavior. We consider a specific instance of this data synthesis problem that addresses minimizing the AV's exposure to adverse environmental conditions during travel to a fixed goal location. The environment is characterized by a threat field, which is a strictly positive scalar field with higher intensities corresponding to hazardous and unfavorable conditions for the AV. We address the problem of synthesizing datasets of minimum exposure paths that resemble a training dataset of such paths. The main contribution of this paper is an inverse reinforcement learning (IRL) model to solve this problem. We consider time-invariant (static) as well as time-varying (dynamic) threat fields. We find that the proposed IRL model provides excellent performance in synthesizing paths from initial conditions not seen in the training dataset, when the threat field is the same as that used for training. Furthermore, we evaluate model performance on unseen threat fields and find low error in that case as well. Finally, we demonstrate the model's ability to synthesize distinct datasets when trained on different datasets with distinct characteristics.