The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber defense, leveraging the collaborative interactions among multiple agents to detect, mitigate, and respond to cyber threats. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios. The results indicate that MADRL can significantly enhance the capability of autonomous cyber defense systems, paving the way for more intelligent cybersecurity strategies. This study contributes to the growing body of knowledge on leveraging artificial intelligence for cybersecurity and sheds light for future research and development in autonomous cyber operations.

翻译：在快速演变的网络威胁环境下，对自主自适应防御机制的需求变得至关重要。多智能体深度强化学习为提高自主网络攻防行动的效能与韧性提供了一种前景广阔的方法。本文探讨了多智能体行动者-评论家算法在网络防御中的应用，该算法为多智能体学习提供了一种通用形式，通过利用多个智能体之间的协同交互来检测、缓解和应对网络威胁。我们在模拟网络攻击场景中证明，每个智能体能够利用多智能体深度强化学习快速学习并自主对抗威胁。结果表明，多智能体深度强化学习可显著提升自主网络防御系统的能力，为更智能的网络安全策略开辟道路。本研究为利用人工智能加强网络安全的知识体系作出贡献，并为自主网络攻防领域的未来研究与发展提供启示。