{"slug": "internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio", "title": "Internalizing Investment Mandates: A Neurosymbolic RL Approach to Portfolio Allocation", "summary": "A paper submitted to arXiv on 12 Nov 2026 presents a PLPG-inspired continuous-action neurosymbolic reinforcement learning method that cuts violations of a six-rule regime-conditional investment mandate to 4.8%, down from 96-98% for unconstrained RL and 33.4% for a reward-penalty baseline. The authors estimate the probability that the policy's own executed action satisfies the mandate using reparameterized Monte Carlo sampling and add that term to the training objective, so compliance is learned by the agent rather than enforced post-hoc at inference time. Audited directly on deterministic mean actions with no correction, the result indicates a continuous-action neurosymbolic policy can substantially internalize a symbolic mandate.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 12 Nov 2026]\n\n# Title:Internalizing Investment Mandates: A Neurosymbolic RL Approach to Portfolio Allocation\n\n[View PDF](http://arxiv.org/pdf/2611.17329v1)\n\n[HTML (experimental)](https://arxiv.org/html/2611.17329v1)\n\nAbstract:Institutional portfolios operate under investment policy statements that specify asset allocation constraints, often conditioned on market regime information. Deep reinforcement learning has been shown to optimize financial performance but typically does not enforce rule compliance, making it unsuitable for real-world deployment. Existing neurosymbolic methods such as Probabilistic Logic Policy Gradient (PLPG) offer constraint-satisfying reinforcement learning (RL) via differentiable symbolic supervision but assume a finite, discrete action space. We present a PLPG-inspired continuous allocation method that estimates the probability that the policy's own executed action satisfies the investment mandate, using reparameterized Monte Carlo sampling, and adds it to the training objective. No post-hoc enforcement mechanism intercepts actions at inference time and compliance is learned by the agent. Audited directly on deterministic mean actions with no correction, the policy reduces violations of a six-rule regime-conditional mandate from 96-98% for unconstrained RL and 33.4% for a reward-penalty baseline to 4.8%, showing that a continuous-action neurosymbolic policy can substantially internalize a symbolic mandate.\n    \n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio", "canonical_source": "http://arxiv.org/abs/2611.17329v1", "published_at": "2026-11-13 15:44:32+00:00", "updated_at": "2026-11-13 16:22:05.988635+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "artificial-intelligence"], "entities": ["arXiv", "Probabilistic Logic Policy Gradient"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio", "markdown": "https://wpnews.pro/news/internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio.md", "text": "https://wpnews.pro/news/internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio.txt", "jsonld": "https://wpnews.pro/news/internalizing-investment-mandates-a-neurosymbolic-rl-approach-to-portfolio.jsonld"}}