# Weaponizing Actions in Multi-Agent Reinforcement Learning: Theoretical and Empirical Study on Security and Robustness

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/weaponizing-actions-in-multi-agent-reinforcement-learning-theoretical-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Tongtong Liu,Joe McCalmon,Md Asifur Rahman,Cameron Lischke,Talal Halabi,Sarra Alqahtani |
| Citations | 2 |
| DOI | 10.1007/978-3-031-21203-1_21 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4308932355 |
| Type | conference-paper |
| Year | 2022 |

## Paper authors

- [Md Asifur Rahman](https://scholariq.org/researchers/md-asifur-rahman/)

## Paper journal

- [Lecture notes in computer science](https://scholariq.org/journals/lecture-notes-in-computer-science/)

## Paper primary topic

- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)

## Paper topics

- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)
- [Smart Grid Security and Resilience](https://scholariq.org/topics/smart-grid-security-and-resilience/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.