4 papers
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
Shae McFadden, Myles Foley, Elizabeth Bates +5
Deep Reinforcement Learning (DRL) has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, tr…
Beyond Rewards in Reinforcement Learning for Cyber Defence
Elizabeth Bates, Chris Hicks, Vasilios Mavroudis
Recent years have seen an explosion of interest in autonomous cyber defence agents trained to defend computer networks using deep reinforcement learning. These agents are typically…
Building Better Environments for Autonomous Cyber Defence
Chris Hicks, Elizabeth Bates, Shae McFadden +12
In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the kn…
Less is more? Rewards in RL for Cyber Defence
Elizabeth Bates, Chris Hicks, Vasilios Mavroudis
The last few years have seen an explosion of interest in autonomous cyber defence agents based on deep reinforcement learning. Such agents are typically trained in a cyber gym envi…