Assuring the Safety of Reinforcement Learning Components: AMLAS-RL

TL;DR

This paper introduces AMLAS-RL, an adaptation of the AMLAS assurance framework, tailored to systematically ensure the safety of reinforcement learning components in cyber-physical systems through an iterative process.

Contribution

We develop AMLAS-RL, a novel framework that extends AMLAS to address the unique safety assurance challenges of reinforcement learning in safety-critical systems.

Findings

Demonstrated AMLAS-RL on a wheeled vehicle example

Provided systematic assurance arguments for RL components

Showed iterative safety validation process

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.