SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910113583857664 |
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| author | Li, Xuyang Maulik, Romit |
| author_facet | Li, Xuyang Maulik, Romit |
| contents | 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 interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation, this work proposes 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, this approach enables interpretability through local stability analysis, where instantaneous growth in action-norms can be predicted before their execution. It is 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15512 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning Li, Xuyang Maulik, Romit Machine Learning 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 interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation, this work proposes 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, this approach enables interpretability through local stability analysis, where instantaneous growth in action-norms can be predicted before their execution. It is 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. |
| title | SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.15512 |