arXiv:2502.14819v4 Announce Type: replace 
Abstract: A long-standing goal in AI is to develop agents capable of solving diverse tasks across a range of environments, including those never seen during training. Two dominant paradigms address this challenge: (i) reinforcement learning (RL), which learns policies via trial and error, and (ii) optimal control, which plans actions using a known or learned dynamics model. However, their comparative strengths in the offline setting - where agents must learn from reward-free trajectories - remain underexplored. In this work, we systematically evaluate RL and control-based methods on a suite of navigation tasks, using offline datasets of varying quality. On the RL side, we consider goal-conditioned and zero-shot methods. On the control side, we train a latent dynamics model using the Joint Embedding Predictive Architecture (JEPA) and employ it for planning. We investigate how factors such as data diversity, trajectory quality, and environment variability influence the performance of these approaches. Our results show that model-free RL benefits most from large amounts of high-quality data, whereas model-based planning generalizes better to unseen layouts and is more data-efficient, while achieving trajectory stitching performance comparable to leading model-free methods. Notably, planning with a latent dynamics model proves to be a strong approach for handling suboptimal offline data and adapting to diverse environments.

تتناول دراسة حديثة إمكانية استخدام بيانات غير متعلقة بالمكافآت لتحسين التخطيط في الذكاء الاصطناعي من خلال نماذج الديناميات الكامنة. يمكن أن يسد هذا النهج الفجوة بين التعلم المعزز والتحكم الأمثل، مما يسمح لوكلاء الذكاء الاصطناعي بالتعامل مع المهام في بيئات غير مألوفة بشكل أكثر فعالية. إن فهم هذه الديناميات أمر بالغ الأهمية، حيث يمكن أن يؤدي إلى أنظمة ذكاء اصطناعي أكثر قوة قادرة على التكيف مع التحديات الجديدة دون الحاجة إلى إعادة تدريب مكثفة.

Un estudio reciente discute el potencial de utilizar datos fuera de línea sin recompensas para mejorar la planificación en inteligencia artificial a través de modelos de dinámicas latentes. Este enfoque podría cerrar la brecha entre el aprendizaje por refuerzo y el control óptimo, permitiendo que los agentes de IA aborden tareas en entornos desconocidos de manera más efectiva. Comprender estas dinámicas es crucial, ya que podría llevar a sistemas de IA más robustos capaces de adaptarse a nuevos desafíos sin necesidad de un extenso reentrenamiento.

Une étude récente aborde le potentiel de l'utilisation de données hors ligne sans récompense pour améliorer la planification en intelligence artificielle grâce à des modèles de dynamiques latentes. Cette approche pourrait combler le fossé entre l'apprentissage par renforcement et le contrôle optimal, permettant aux agents d'IA de relever des tâches dans des environnements inconnus plus efficacement. Comprendre ces dynamiques est crucial car cela pourrait conduire à des systèmes d'IA plus robustes capables de s'adapter à de nouveaux défis sans nécessiter de réentraînement extensif.

A recent study discusses the potential of using reward-free offline data to enhance planning in artificial intelligence through latent dynamics models. This approach could bridge the gap between reinforcement learning and optimal control, allowing AI agents to tackle tasks in unfamiliar environments more effectively. Understanding these dynamics is crucial as it could lead to more robust AI systems capable of adapting to new challenges without extensive retraining.

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

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

One More Thing in AI – Your Shortcut to AI Mastery

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

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