6 papers
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
Sanyam Vyas, Alberto Caron, Chris Hicks +2
Deep Reinforcement Learning (DRL) systems are increasingly used in safety-critical applications, yet their security remains severely underexplored. This work investigates backdoor…
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Alberto Caron, Chris Hicks, Vasilios Mavroudis
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motiv…
Towards Causal Model-Based Policy Optimization
Alberto Caron, Vasilios Mavroudis, Chris Hicks
Real-world decision-making problems are often marked by complex, uncertain dynamics that can shift or break under changing conditions. Traditional Model-Based Reinforcement Learnin…
Guidelines for Applying RL and MARL in Cybersecurity Applications
Vasilios Mavroudis, Gregory Palmer, Sara Farmer +6
Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) have emerged as promising methodologies for addressing challenges in automated cyber defence (ACD). These…
Entity-based Reinforcement Learning for Autonomous Cyber Defence
Isaac Symes Thompson, Alberto Caron, Chris Hicks +1
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is…
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
Benjamin Kolicic, Alberto Caron, Chris Hicks +1
In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly…