# Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation

arXiv: https://arxiv.org/abs/2312.12861 · HTML: https://mlsanity.com/paper/2312.12861/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks](https://mlsanity.com/paper/2412.04153/) (2024)
- [Safe Reinforcement Learning using Action Projection: Safeguard the Policy or the Environment?](https://mlsanity.com/paper/2509.12833/) (2025)
- [ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks](https://mlsanity.com/paper/2506.22423/) (2025)
- [Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control](https://mlsanity.com/paper/2509.15799/) (2025)
- [Constraint-Aware Reinforcement Learning via Adaptive Action Scaling](https://mlsanity.com/paper/2510.11491/) (2025)
- [High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning](https://mlsanity.com/paper/2607.03512/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning~(MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training.