# AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2212.10064 · HTML: https://mlsanity.com/paper/2212.10064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04737/) (2019)
- [Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.04402/) (2021)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.12127/) (2019)
- [Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions](https://mlsanity.com/paper/1502.06030/) (2015)
- [Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments](https://mlsanity.com/paper/2105.00389/) (2021)
- [Learning Robot Swarm Tactics over Complex Adversarial Environments](https://mlsanity.com/paper/2109.05663/) (2021)

## Built on by (most cited first, 3 of 3)

- [Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit](https://mlsanity.com/paper/2409.17443/) (2024)
- [Learning What Matters Now: A Dual-Critic Context-Aware RL Framework for Priority-Driven Information Gain](https://mlsanity.com/paper/2506.06786/) (2025)
- [Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2601.05407/) (2026)

## Abstract (arXiv)

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.