arXiv:2511.01374v1 Announce Type: new 
Abstract: Traditional continuous deep reinforcement learning (RL) algorithms employ deterministic or unimodal Gaussian actors, which cannot express complex multimodal decision distributions. This limitation can hinder their performance in diversity-critical scenarios. There have been some attempts to design online multimodal RL algorithms based on diffusion or amortized actors. However, these actors are intractable, making existing methods struggle with balancing performance, decision diversity, and efficiency simultaneously. To overcome this challenge, we first reformulate existing intractable multimodal actors within a unified framework, and prove that they can be directly optimized by policy gradient via reparameterization. Then, we propose a distance-based diversity regularization that does not explicitly require decision probabilities. We identify two diversity-critical domains, namely multi-goal achieving and generative RL, to demonstrate the advantages of multimodal policies and our method, particularly in terms of few-shot robustness. In conventional MuJoCo benchmarks, our algorithm also shows competitive performance. Moreover, our experiments highlight that the amortized actor is a promising policy model class with strong multimodal expressivity and high performance. Our code is available at https://github.com/PneuC/DrAC

تقدم دراسة جديدة طرقًا مبتكرة لتعلم التعزيز العميق تتناول قيود الخوارزميات التقليدية، التي غالبًا ما تواجه صعوبة في سيناريوهات اتخاذ القرار المعقدة. من خلال التركيز على السياسات متعددة الأوجه ودمج تنظيم التنوع، يمكن أن تعزز هذه الأبحاث بشكل كبير أداء أنظمة التعلم المعزز في بيئات متنوعة. هذه الخطوة مهمة لأنها تفتح آفاقًا جديدة للتطبيقات في مجالات تتطلب اتخاذ قرارات دقيقة، مثل الروبوتات والأنظمة المستقلة.

Un nuevo estudio presenta métodos innovadores para el aprendizaje por refuerzo profundo que abordan las limitaciones de los algoritmos tradicionales, que a menudo luchan con escenarios de toma de decisiones complejos. Al centrarse en políticas multimodales e incorporar la regularización de la diversidad, esta investigación podría mejorar significativamente el rendimiento de los sistemas de aprendizaje por refuerzo en entornos diversos. Este avance es crucial ya que abre nuevas posibilidades para aplicaciones en campos que requieren toma de decisiones matizadas, como la robótica y los sistemas autónomos.

Une nouvelle étude présente des méthodes innovantes pour l'apprentissage par renforcement profond qui s'attaquent aux limites des algorithmes traditionnels, souvent en difficulté dans des scénarios de prise de décision complexes. En se concentrant sur des politiques multimodales et en intégrant une régularisation de la diversité, cette recherche pourrait améliorer considérablement les performances des systèmes d'apprentissage par renforcement dans des environnements divers. Cette avancée est cruciale car elle ouvre de nouvelles possibilités d'applications dans des domaines nécessitant une prise de décision nuancée, comme la robotique et les systèmes autonomes.

A new study introduces innovative methods for deep reinforcement learning that tackle the limitations of traditional algorithms, which often struggle with complex decision-making scenarios. By focusing on multimodal policies and incorporating diversity regularization, this research could significantly enhance the performance of RL systems in diverse environments. This advancement is crucial as it opens up new possibilities for applications in fields requiring nuanced decision-making, such as robotics and autonomous systems.

Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization

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Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization

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