# MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding

arXiv: https://arxiv.org/abs/1910.12639 · HTML: https://mlsanity.com/paper/1910.12639/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 20 of 20)

- [Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates](https://mlsanity.com/paper/2101.05436/) (2021)
- [Multi-Agent Constrained Policy Optimisation](https://mlsanity.com/paper/2110.02793/) (2021)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Online Shielding for Reinforcement Learning](https://mlsanity.com/paper/2212.01861/) (2022)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning](https://mlsanity.com/paper/2405.13863/) (2024)
- [Shielding Atari Games with Bounded Prescience](https://mlsanity.com/paper/2101.08153/) (2021)
- [Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios](https://mlsanity.com/paper/2210.02300/) (2022)
- [Compositional Shielding and Reinforcement Learning for Multi-Agent Systems](https://mlsanity.com/paper/2410.10460/) (2024)
- [Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL](https://mlsanity.com/paper/2504.15425/) (2025)
- [Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces](https://mlsanity.com/paper/2108.03952/) (2021)
- [A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks](https://mlsanity.com/paper/2412.04153/) (2024)
- [Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts](https://mlsanity.com/paper/2107.12942/) (2021)
- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04867/) (2024)
- [Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics](https://mlsanity.com/paper/2501.12061/) (2025)
- [Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review](https://mlsanity.com/paper/2507.10142/) (2025)
- [Safe Learning of Uncertain Environments](https://mlsanity.com/paper/2103.01413/) (2021)
- [Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles](https://mlsanity.com/paper/2302.04321/) (2023)
- [Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering](https://mlsanity.com/paper/2606.07193/) (2026)

## Abstract (arXiv)

Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails to guarantee even simple safety properties such as obstacle avoidance. To ensure safety, we propose multi-agent model predictive shielding (MAMPS), an algorithm that provably guarantees safety for an arbitrary learned policy. In particular, it operates by using the learned policy as often as possible, but instead uses a backup policy in cases where it cannot guarantee the safety of the learned policy. Using a multi-agent simulation environment, we show how MAMPS can achieve good performance while ensuring safety.