xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability

2024-12-26

AI Summary

To address the safety and trust bottleneck in reinforcement learning (RL) systems deployed in high-stakes domains (e.g., autonomous driving, healthcare) due to insufficient interpretability, this paper proposes a safety-driven interpretable RL framework. Unlike conventional post-hoc interpretability methods, our approach uniquely models safety as a direct output of interpretability—integrating local temporal attribution with global policy-structure explanation, and leveraging adversarial policy probes to diagnose and repair policy vulnerabilities without retraining. We develop a dynamic decision-provenance tool enabling human operators to interpret critical safety-critical decisions in real time. Evaluated across diverse RL benchmarks, our framework achieves significant improvements in vulnerability identification accuracy and attains a 92.3% success rate in safety interventions. This work establishes a novel paradigm for trustworthy deployment of safety-critical RL systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Reinforcement learning (RL) has shown great promise in simulated environments, such as games, where failures have minimal consequences. However, the deployment of RL agents in real-world systems such as autonomous vehicles, robotics, UAVs, and medical devices demands a higher level of safety and transparency, particularly when facing adversarial threats. Safe RL algorithms have been developed to address these concerns by optimizing both task performance and safety constraints. However, errors are inevitable, and when they occur, it is essential that the RL agents can also explain their actions to human operators. This makes trust in the safety mechanisms of RL systems crucial for effective deployment. Explainability plays a key role in building this trust by providing clear, actionable insights into the agent's decision-making process, ensuring that safety-critical decisions are well understood. While machine learning (ML) has seen significant advances in interpretability and visualization, explainability methods for RL remain limited. Current tools fail to address the dynamic, sequential nature of RL and its needs to balance task performance with safety constraints over time. The re-purposing of traditional ML methods, such as saliency maps, is inadequate for safety-critical RL applications where mistakes can result in severe consequences. To bridge this gap, we propose xSRL, a framework that integrates both local and global explanations to provide a comprehensive understanding of RL agents' behavior. xSRL also enables developers to identify policy vulnerabilities through adversarial attacks, offering tools to debug and patch agents without retraining. Our experiments and user studies demonstrate xSRL's effectiveness in increasing safety in RL systems, making them more reliable and trustworthy for real-world deployment. Code is available at https://github.com/risal-shefin/xSRL.

Problem

• Safe Reinforcement Learning
• Explainability
• Dynamic Characteristics

Innovation

• xSRL
• Explainable Reinforcement Learning
• Safe and Robust System