🤖 AI Summary
To address the safety verification challenge for deep reinforcement learning (DRL) decision-support systems prior to deployment, this paper proposes the first explainable and intervenable adversarial analysis framework tailored for the pre-deployment phase. Methodologically, it integrates temporal sensitivity modeling with joint observation-dimension ranking and leverages a customized strategic simulation environment—CyberStrike—to generate precise temporal perturbations, enabling behavioral pattern identification and vulnerability localization. Key contributions include: (1) establishing a novel paradigm for DRL policy vulnerability assessment; (2) introducing a joint observation-temporal sensitivity analysis method; and (3) empirically demonstrating cross-algorithm and cross-architecture attack transferability. Experiments reveal that mainstream DRL policies exhibit high sensitivity to minute perturbations at critical decision steps, exposing widespread robustness deficiencies—providing actionable insights for DRL system hardening.
📝 Abstract
This paper introduces a comprehensive framework designed to analyze and secure decision-support systems trained with Deep Reinforcement Learning (DRL), prior to deployment, by providing insights into learned behavior patterns and vulnerabilities discovered through simulation. The introduced framework aids in the development of precisely timed and targeted observation perturbations, enabling researchers to assess adversarial attack outcomes within a strategic decision-making context. We validate our framework, visualize agent behavior, and evaluate adversarial outcomes within the context of a custom-built strategic game, CyberStrike. Utilizing the proposed framework, we introduce a method for systematically discovering and ranking the impact of attacks on various observation indices and time-steps, and we conduct experiments to evaluate the transferability of adversarial attacks across agent architectures and DRL training algorithms. The findings underscore the critical need for robust adversarial defense mechanisms to protect decision-making policies in high-stakes environments.