SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning

[2502.15512] SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2502.15512 (cs) [Submitted on 21 Feb 2025 ( v1 ), last revised 8 Apr 2026 (this version, v5)] Title: SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning Authors: Xuyang Li , Romit Maulik View a PDF of the paper titled SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning, by Xuyang Li and 1 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2502.15512 [cs.LG] (or arXiv:2502.15512v5 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2502.15512 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xuyang Li [ view email ] [v1] Fri, 21 Feb 2025 15:09:39 UTC (2,407 KB) [v2] Sun, 25 May 2025 23:56:50 UTC (2,355 KB) [v3] Mon, 18 Aug 2025 04:36:51 UTC (3,306 KB) [v4] Wed, 18 Mar 2026 03:10:09 UTC (3,532 KB) [v5] Wed, 8 Apr 2026 20:41:44 UTC (3,539 KB) Full-text links: Access Paper: View a PDF of the paper titled SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning, by Xuyang Li and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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