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September 10, 2025Applied AI LettersOpen Access

Multi‐Objective Reinforcement Learning for Automated Resilient Cyber Defence

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Multi-objective reinforcement learning improves network defense by considering multiple factors simultaneously.
Performance comparison reveals that MORL agents outperform single-objective approaches in managing network defense complexities.
The approach involves using two algorithms, MOPPO and PCN, to create agents capable of balancing conflicting objectives.
This research emphasizes the need for adaptive models that can address the dynamic nature of cybersecurity threats.

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Multi-objective RL models defend networks against red agents while maintaining green agents, highlighting a new approach.

O’Driscoll et al. (2025) studied this question.

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