Context-Aware Multi-Agent Reinforcement Learning for Cyber Defense: Problem Formulation and Literature Review

Computer networks face attacks that change quickly and come from many directions at once, and a single automated defender often cannot keep up. Multi-agent reinforcement learning (MARL) offers a way to place several learning defenders across a network so that they can act at the same time and still work as a team. However, most existing MARL cyber-defense agents make decisions from raw, low-level observations and do not explicitly use “context” information such as how valuable an asset is, how much traffic looks abnormal right now, or what stage an attack has reached. This paper looks closely at this gap. We give a clear, formal statement of the cyber-defense problem as a context-aware, decentralized, partially observable Markov decision process (Dec-POMDP), and we review the research that has been done on multi-agent reinforcement learning, cyber-defense simulators, and context-aware learning. We bring these three areas together, point out where they overlap and where they do not, and summarize the gap in a simple comparison table. We also list the main open problems, such as handling constantly changing attacker behavior, scaling to large networks, and making the learned policies easy to trust and explain. The paper does not present a solution or run experiments; instead, it lays the groundwork a precise problem definition and an organized picture of prior work for building context-aware MARL defenders in future studies.