# Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling

arXiv: https://arxiv.org/abs/2004.13439 · HTML: https://mlsanity.com/paper/2004.13439/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem](https://mlsanity.com/paper/2204.03061/) (2022)
- [Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem](https://mlsanity.com/paper/2605.10257/) (2026)

## Abstract (arXiv)

We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy. These hypotheses need to be confirmed by future work. If confirmed, they hold promises with respect to optimizing highly efficient logistics ecosystems like the Swiss Federal Railways railway network.