# Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems arXiv: https://arxiv.org/abs/2206.10158 ยท HTML: https://mlsanity.com/paper/2206.10158/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 50 references that are in the map. - First posted on arXiv 21 Jun 2022. ## Builds on (most cited first, 30 of 37) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017) - [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019) - [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019) - [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018) - [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018) - [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016) - [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017) - [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016) - [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models](https://mlsanity.com/paper/1810.12715/) (2018) - [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017) - [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016) - [Certified Defenses against Adversarial Examples](https://mlsanity.com/paper/1801.09344/) (2018) - [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018) - [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020) - [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017) - [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020) - [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018) - [Adversarial Policies: Attacking Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10615/) (2019) - [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018) - [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019) - [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021) - [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020) - [Robust Deep Reinforcement Learning through Adversarial Loss](https://mlsanity.com/paper/2008.01976/) (2020) - [Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation](https://mlsanity.com/paper/1911.09272/) (2019) - [Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL](https://mlsanity.com/paper/2106.05087/) (2021) - [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021) - [Certified Adversarial Robustness for Deep Reinforcement Learning](https://mlsanity.com/paper/1910.12908/) (2019) - [CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing](https://mlsanity.com/paper/2106.09292/) (2021) ## Built on by (most cited first, 7 of 7) - [What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2212.02705/) (2022) - [Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.04875/) (2023) - [Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.11746/) (2022) - [Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL](https://mlsanity.com/paper/2305.17342/) (2023) - [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023) - [RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation](https://mlsanity.com/paper/2512.24268/) (2025) - [Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems](https://mlsanity.com/paper/2311.00859/) (2023) ## Abstract (arXiv) Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored. Specifically, if communication messages are manipulated by malicious attackers, agents relying on untrustworthy communication may take unsafe actions that lead to catastrophic consequences. Therefore, it is crucial to ensure that agents will not be misled by corrupted communication, while still benefiting from benign communication. In this work, we consider an environment with N agents, where the attacker may arbitrarily change the communication from any C