Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However, real-world control systems--especially those requiring precise and reliable performance--often demand formal stability, and existing DRL approaches typically lack explicit mechanisms to ensure or analyze stability. To address this limitation, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions), a novel RL framework that models control actions as dynamic, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system, our approach enables both stability analysis and interpretability. We demonstrated that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation, SALSA-RL provides a powerful tool for advancing the design, analysis, and theoretical understanding of RL systems.
View on arXiv@article{li2025_2502.15512,
title={ SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning },
author={ Xuyang Li and Romit Maulik },
journal={arXiv preprint arXiv:2502.15512},
year={ 2025 }
}