Collaborative Reinforcement Learning for Cyber Defense: Analysis of Strategies, and Policies

As cybersecurity threats grow in scale and sophistication, traditional defenses increasingly struggle to detect and counter them. Recent work applies reinforcement learning (RL) to develop adaptive defensive agents, but challenges remain, particularly in how agents learn, the environments used, and the strategies they adopt. These issues are amplified in multi-agent settings, where coordination becomes especially complex. This paper presents an empirical analysis of collaborative RL for cybersecurity defense, focusing on environment models, RL methods, and agent policies. Specifically, it compares several multi-agent RL algorithms in the context of CAGE Challenge 4 to identify effective defense configurations. The study also evaluates the learned policies to assess their real-world applicability and highlight gaps between agent behavior and practical defense strategies.