# Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game

arXiv: https://arxiv.org/abs/2305.12872 · HTML: https://mlsanity.com/paper/2305.12872/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2023.

## Built on by (most cited first, 18 of 18)

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- [Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks](https://mlsanity.com/paper/2407.08164/) (2024)
- [Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks](https://mlsanity.com/paper/2308.00958/) (2023)
- [Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization](https://mlsanity.com/paper/2310.09833/) (2023)
- [Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.02844/) (2025)
- [Towards Fault Tolerance in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.00534/) (2024)
- [Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security](https://mlsanity.com/paper/2506.22445/) (2025)
- [Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.11824/) (2025)
- [Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18024/) (2026)
- [Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games](https://mlsanity.com/paper/2411.04976/) (2024)
- [Safe Exploitative Play with Untrusted Type Beliefs](https://mlsanity.com/paper/2411.07679/) (2024)
- [Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.15103/) (2025)
- [Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale](https://mlsanity.com/paper/2512.18561/) (2025)
- [Bayesian Robust Financial Trading with Adversarial Synthetic Market Data](https://mlsanity.com/paper/2601.17008/) (2026)
- [Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization](https://mlsanity.com/paper/2602.11437/) (2026)
- [BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2606.20701/) (2026)
- [Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks](https://mlsanity.com/paper/2608.06520/) (2026)

## Abstract (arXiv)

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions due to malfunction or adversarial attack. To address the uncertainty that any agent can be adversarial, we propose a Bayesian Adversarial Robust Dec-POMDP (BARDec-POMDP) framework, which views Byzantine adversaries as nature-dictated types, represented by a separate transition. This allows agents to learn policies grounded on their posterior beliefs about the type of other agents, fostering collaboration with identified allies and minimizing vulnerability to adversarial manipulation. We define the optimal solution to the BARDec-POMDP as an ex post robust Bayesian Markov perfect equilibrium, which we proof to exist and weakly dominates the equilibrium of previous robust MARL approaches. To realize this equilibrium, we put forward a two-timescale actor-critic algorithm with almost sure convergence under specific conditions. Experimentation on matrix games, level-based foraging and StarCraft II indicate that, even under worst-case perturbations, our method successfully acquires intricate micromanagement skills and adaptively aligns with allies, demonstrating resilience against non-oblivious adversaries, random allies, observation-based attacks, and transfer-based attacks.