arXiv:2510.25311v1 Announce Type: new 
Abstract: Reinforcement Learning algorithms are primarily focused on learning a policy that maximizes expected return. As a result, the learned policy can exploit one or few reward sources. However, in many natural situations, it is desirable to learn a policy that induces a dispersed marginal state distribution over rewarding states, while maximizing the expected return which is typically tied to reaching a goal state. This aspect remains relatively unexplored. Existing techniques based on entropy regularization and intrinsic rewards use stochasticity for encouraging exploration to find an optimal policy which may not necessarily lead to dispersed marginal state distribution over rewarding states. Other RL algorithms which match a target distribution assume the latter to be available apriori. This may be infeasible in large scale systems where enumeration of all states is not possible and a state is determined to be a goal state only upon reaching it. We formalize the problem of maximizing the expected return while uniformly visiting the goal states as Multi Goal RL in which an oracle classifier over the state space determines the goal states. We propose a novel algorithm that learns a high-return policy mixture with marginal state distribution dispersed over the set of goal states. Our algorithm is based on optimizing a custom RL reward which is computed - based on the current policy mixture - at each iteration for a set of sampled trajectories. The latter are used via an offline RL algorithm to update the policy mixture. We prove performance guarantees for our algorithm, showing efficient convergence bounds for optimizing a natural objective which captures the expected return as well as the dispersion of the marginal state distribution over the goal states. We design and perform experiments on synthetic MDPs and standard RL environments to evaluate the effectiveness of our algorithm.

تناقش ورقة جديدة على arXiv التقدم في التعلم المعزز متعدد الأهداف، مع تسليط الضوء على الحاجة إلى خوارزميات لا تعظم العوائد فحسب، بل تضمن أيضًا توزيعًا متنوعًا للمكافآت. هذه الأبحاث مهمة لأنها تعالج قيود طرق التعلم المعزز التقليدية، التي غالبًا ما تركز على مصدر واحد أو عدد قليل من مصادر المكافأة. من خلال تعزيز استكشاف أوسع للحالات المكافئة، يمكن أن يؤدي هذا النهج إلى استراتيجيات تعلم أكثر فعالية في البيئات المعقدة.

Un nuevo artículo en arXiv discute los avances en el aprendizaje por refuerzo multiobjetivo, destacando la necesidad de algoritmos que no solo maximicen los retornos, sino que también aseguren una distribución diversa de recompensas. Esta investigación es significativa ya que aborda las limitaciones de los métodos tradicionales de aprendizaje por refuerzo, que a menudo se centran en una o pocas fuentes de recompensa. Al promover una exploración más amplia de los estados recompensantes, este enfoque podría llevar a estrategias de aprendizaje más efectivas en entornos complejos.

Un nouvel article sur arXiv aborde les avancées dans l'apprentissage par renforcement multi-objectifs, soulignant la nécessité d'algorithmes qui maximisent non seulement les retours mais garantissent également une distribution diversifiée des récompenses. Cette recherche est importante car elle traite des limites des méthodes d'apprentissage par renforcement traditionnelles, qui se concentrent souvent sur une ou quelques sources de récompense. En favorisant une exploration plus large des états récompensants, cette approche pourrait conduire à des stratégies d'apprentissage plus efficaces dans des environnements complexes.

A new paper on arXiv discusses advancements in multi-goal reinforcement learning, highlighting the need for algorithms that not only maximize returns but also ensure a diverse distribution of rewards. This research is significant as it addresses the limitations of traditional reinforcement learning methods, which often focus on a single or few reward sources. By promoting a broader exploration of rewarding states, this approach could lead to more effective learning strategies in complex environments.

Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning

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

Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning

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