Assuring the Safety of Reinforcement Learning Components: AMLAS-RL

2025-07-08

AI Summary

Safety-critical cyber-physical systems (CPS) lack end-to-end safety assurance for reinforcement learning (RL) components; existing safe-RL approaches lack systematic safety assurance structures, while the Assurance Case–based AMLAS framework is ill-suited to RL’s dynamic decision-making and interactive behavior. Method: This paper proposes AMLAS-RL—a novel assurance framework that systematically adapts the structured assurance argument methodology of AMLAS to RL. It establishes a closed-loop safety evidence chain spanning requirements specification, policy training, verification & validation, and deployment & operation. By integrating RL algorithms, verifiable safety constraints, and an iterative assurance argument generation process, it enables end-to-end traceability from formal safety goals to auditable evidence. Contribution/Results: AMLAS-RL is the first framework to instantiate AMLAS for RL. Its feasibility and practicality are demonstrated through a complete assurance case for obstacle-avoidance navigation in a wheeled mobile robot, achieving full闭环 evidence generation and validation.

Technology Category

• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Multiagent Learning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities, incorporating ML components introduces significant safety and assurance challenges. Among ML techniques, reinforcement learning (RL) is particularly suited for CPS due to its capacity to handle complex, dynamic environments where explicit models of interaction between system and environment are unavailable or difficult to construct. However, in safety-critical applications, this learning process must not only be effective but demonstrably safe. Safe-RL methods aim to address this by incorporating safety constraints during learning, yet they fall short in providing systematic assurance across the RL lifecycle. The AMLAS methodology offers structured guidance for assuring the safety of supervised learning components, but it does not directly apply to the unique challenges posed by RL. In this paper, we adapt AMLAS to provide a framework for generating assurance arguments for an RL-enabled system through an iterative process; AMLAS-RL. We demonstrate AMLAS-RL using a running example of a wheeled vehicle tasked with reaching a target goal without collision.

Problem

• Ensuring safety in reinforcement learning for cyber-physical systems
• Addressing lack of systematic assurance in RL lifecycle
• Adapting AMLAS methodology to RL-specific safety challenges

Innovation

• Adapting AMLAS for RL safety assurance
• Iterative framework for RL-enabled systems
• Safe-RL with systematic lifecycle assurance