arXiv:2511.00806v1 Announce Type: new 
Abstract: Cyber-physical systems (CPS) require the joint optimization of discrete cyber actions and continuous physical parameters under stringent safety logic constraints. However, existing hierarchical approaches often compromise global optimality, whereas reinforcement learning (RL) in hybrid action spaces often relies on brittle reward penalties, masking, or shielding and struggles to guarantee constraint satisfaction. We present logic-informed reinforcement learning (LIRL), which equips standard policy-gradient algorithms with projection that maps a low-dimensional latent action onto the admissible hybrid manifold defined on-the-fly by first-order logic. This guarantees feasibility of every exploratory step without penalty tuning. Experimental evaluations have been conducted across multiple scenarios, including industrial manufacturing, electric vehicle charging stations, and traffic signal control, in all of which the proposed method outperforms existing hierarchical optimization approaches. Taking a robotic reducer assembly system in industrial manufacturing as an example, LIRL achieves a 36.47\% to 44.33\% reduction at most in the combined makespan-energy objective compared to conventional industrial hierarchical scheduling methods. Meanwhile, it consistently maintains zero constraint violations and significantly surpasses state-of-the-art hybrid-action reinforcement learning baselines. Thanks to its declarative logic-based constraint formulation, the framework can be seamlessly transferred to other domains such as smart transportation and smart grid, thereby paving the way for safe and real-time optimization in large-scale CPS.

تقدم دراسة جديدة نهجًا مستنيرًا بالمنطق في التعلم المعزز يهدف إلى تحسين الأنظمة السيبرانية الفيزيائية على نطاق واسع. تتناول هذه الطريقة التحديات المتمثلة في تحقيق التوازن بين الإجراءات السيبرانية المنفصلة والمعلمات الفيزيائية المستمرة مع الالتزام بقيود منطقية صارمة للسلامة. على عكس الأساليب الهرمية التقليدية التي قد تضحي بالتحسين العالمي، يعد هذا النهج المبتكر بتحسين الكفاءة والموثوقية في الأنظمة المعقدة، مما يمثل تقدمًا كبيرًا في هذا المجال.

Un nuevo estudio presenta un enfoque de aprendizaje por refuerzo informado por la lógica, destinado a optimizar sistemas ciberfísicos a gran escala. Este método aborda los desafíos de equilibrar acciones cibernéticas discretas con parámetros físicos continuos, cumpliendo con estrictas restricciones lógicas de seguridad. A diferencia de los métodos jerárquicos tradicionales que pueden sacrificar la optimalidad global, este enfoque innovador promete mejorar la eficiencia y la fiabilidad en sistemas complejos, lo que representa un avance significativo en el campo.

Une nouvelle étude présente une approche d'apprentissage par renforcement informée par la logique, visant à optimiser les systèmes cyber-physiques à grande échelle. Cette méthode répond aux défis de l'équilibre entre les actions cybernétiques discrètes et les paramètres physiques continus tout en respectant des contraintes logiques de sécurité strictes. Contrairement aux méthodes hiérarchiques traditionnelles qui peuvent sacrifier l'optimalité globale, cette approche innovante promet d'améliorer l'efficacité et la fiabilité des systèmes complexes, ce qui constitue une avancée significative dans le domaine.

A new study introduces a logic-informed reinforcement learning approach aimed at optimizing large-scale cyber-physical systems. This method addresses the challenges of balancing discrete cyber actions with continuous physical parameters while adhering to strict safety logic constraints. Unlike traditional hierarchical methods that may sacrifice global optimality, this innovative approach promises to enhance efficiency and reliability in complex systems, making it a significant advancement in the field.

Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems

arXiv:2601.08247v1 Announce Type: new 
Abstract: Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.

دراسة حديثة نُشرت على arXiv تستكشف دمج التحيزات المعرفية في أطر التعلم المعزز (RL) لاتخاذ القرارات المالية، مشددةً على كيفية تأثير سلوك الإنسان المتأثر بتحيزات مثل الثقة الزائدة والقلق من الخسارة على استراتيجيات التداول. تهدف الأبحاث إلى إثبات أن نماذج التعلم المعزز التي تتضمن هذه التحيزات يمكن أن تحقق عوائد معدلة للمخاطر أفضل مقارنة بالنماذج التقليدية التي تفترض العقلانية.

Un estudio reciente publicado en arXiv explora la integración de sesgos cognitivos en los marcos de aprendizaje por refuerzo (RL) para la toma de decisiones financieras, destacando cómo el comportamiento humano influenciado por sesgos como la sobreconfianza y la aversión a la pérdida puede afectar las estrategias de trading. La investigación busca demostrar que los modelos de RL que incorporan estos sesgos pueden lograr mejores rendimientos ajustados al riesgo en comparación con los modelos tradicionales que asumen racionalidad.

Une étude récente publiée sur arXiv examine l'intégration des biais cognitifs dans les cadres d'apprentissage par renforcement (RL) pour la prise de décision financière, soulignant comment le comportement humain influencé par des biais tels que la surconfiance et l'aversion à la perte peut affecter les stratégies de trading. La recherche vise à démontrer que les modèles RL intégrant ces biais peuvent obtenir de meilleurs rendements ajustés au risque par rapport aux modèles traditionnels qui supposent la rationalité.

A recent study published on arXiv explores the integration of cognitive biases into reinforcement learning (RL) frameworks for financial decision-making, highlighting how human behavior influenced by biases like overconfidence and loss aversion can affect trading strategies. The research aims to demonstrate that RL models incorporating these biases can achieve better risk-adjusted returns compared to traditional models that assume rationality.

Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making

arXiv:2510.21060v2 Announce Type: replace 
Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning robotics, healthcare, and large language model training. The increasing deployment of PO in sensitive domains, however, raises significant privacy concerns. In this paper, we initiate a theoretical study of differentially private policy optimization, focusing explicitly on its sample complexity. We first formalize an appropriate definition of differential privacy (DP) tailored to PO, addressing the inherent challenges arising from on-policy learning dynamics and the subtlety involved in defining the unit of privacy. We then systematically analyze the sample complexity of widely-used PO algorithms, including policy gradient (PG), natural policy gradient (NPG) and more, under DP constraints and various settings, via a unified framework. Our theoretical results demonstrate that privacy costs can often manifest as lower-order terms in the sample complexity, while also highlighting subtle yet important observations in private PO settings. These offer valuable practical insights for privacy-preserving PO algorithms.

تم نشر دراسة حديثة حول تحسين السياسات بشكل مختلف (DPPO) تركز على تعقيد العينة في تحسين السياسات (PO) في التعلم المعزز (RL). تتناول هذه الدراسة المخاوف المتعلقة بالخصوصية في التطبيقات الحساسة مثل الروبوتات والرعاية الصحية من خلال صياغة تعريف للخصوصية المختلفة يتناسب مع PO وتحليل تعقيد العينة لعدة خوارزميات PO تحت قيود DP.

Se ha publicado un estudio reciente sobre la optimización de políticas diferencialmente privadas (DPPO), centrado en la complejidad de muestra de la optimización de políticas (PO) en el aprendizaje por refuerzo (RL). Esta investigación aborda las preocupaciones de privacidad en aplicaciones sensibles como la robótica y la atención médica al formalizar una definición de privacidad diferencial adaptada a la PO y analizar la complejidad de muestra de varios algoritmos de PO bajo restricciones de DP.

Une étude récente sur l'optimisation de politiques différemment privées (DPPO) a été publiée, se concentrant sur la complexité d'échantillonnage de l'optimisation de politiques (PO) dans l'apprentissage par renforcement (RL). Cette recherche aborde les préoccupations en matière de confidentialité dans des applications sensibles telles que la robotique et les soins de santé en formalisant une définition de la confidentialité différentielle adaptée à la PO et en analysant la complexité d'échantillonnage de divers algorithmes de PO sous des contraintes de DP.

A recent study on differentially private policy optimization (DPPO) has been published, focusing on the sample complexity of policy optimization (PO) in reinforcement learning (RL). This research addresses privacy concerns in sensitive applications such as robotics and healthcare by formalizing a definition of differential privacy tailored to PO and analyzing the sample complexity of various PO algorithms under DP constraints.

On the Sample Complexity of Differentially Private Policy Optimization

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Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems

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