arXiv:2511.14220v1 Announce Type: new 
Abstract: Model-based reinforcement learning (RL) methods that leverage search are responsible for many milestone breakthroughs in RL. Sequential Monte Carlo (SMC) recently emerged as an alternative to the Monte Carlo Tree Search (MCTS) algorithm which drove these breakthroughs. SMC is easier to parallelize and more suitable to GPU acceleration. However, it also suffers from large variance and path degeneracy which prevent it from scaling well with increased search depth, i.e., increased sequential compute. To address these problems, we introduce Twice Sequential Monte Carlo Tree Search (TSMCTS). Across discrete and continuous environments TSMCTS outperforms the SMC baseline as well as a popular modern version of MCTS. Through variance reduction and mitigation of path degeneracy, TSMCTS scales favorably with sequential compute while retaining the properties that make SMC natural to parallelize.

يقدم المقال طريقة البحث في الشجرة المعروفة باسم TSMCTS (بحث شجرة مونت كارلو التسلسلي المزدوج)، وهي نهج جديد في التعلم المعزز القائم على النماذج، حيث تحسن على طرق مونت كارلو التسلسلي (SMC). تعالج TSMCTS مشكلات التباين العالي وتدهور المسارات التي تعيق قابلية SMC للتوسع مع زيادة عمق البحث. من خلال تحسين تقليل التباين والتخفيف من تدهور المسارات، تظهر TSMCTS أداءً متفوقًا في البيئات المنفصلة والمستمرة مقارنةً بـ SMC وخوارزميات بحث شجرة مونت كارلو الحديثة (MCTS).

El artículo presenta el Twice Sequential Monte Carlo Tree Search (TSMCTS), un enfoque novedoso en el aprendizaje por refuerzo basado en modelos que mejora los métodos de Monte Carlo Secuencial (SMC). TSMCTS aborda problemas de alta varianza y degeneración de caminos que obstaculizan la escalabilidad de SMC con una mayor profundidad de búsqueda. Al mejorar la reducción de varianza y mitigar la degeneración de caminos, TSMCTS demuestra un rendimiento superior en entornos discretos y continuos en comparación con SMC y los algoritmos modernos de Monte Carlo Tree Search (MCTS).

L'article présente le Twice Sequential Monte Carlo Tree Search (TSMCTS), une nouvelle approche dans l'apprentissage par renforcement basé sur des modèles qui améliore les méthodes de Monte Carlo séquentiel (SMC). TSMCTS résout les problèmes de haute variance et de dégénérescence des chemins qui entravent l'évolutivité de SMC avec une profondeur de recherche accrue. En améliorant la réduction de variance et en atténuant la dégénérescence des chemins, TSMCTS montre des performances supérieures dans des environnements discrets et continus par rapport à SMC et aux algorithmes modernes de Monte Car…

The article introduces Twice Sequential Monte Carlo Tree Search (TSMCTS), a novel approach in model-based reinforcement learning that improves upon Sequential Monte Carlo (SMC) methods. TSMCTS addresses issues of high variance and path degeneracy that hinder SMC's scalability with increased search depth. By enhancing variance reduction and mitigating path degeneracy, TSMCTS demonstrates superior performance in both discrete and continuous environments compared to SMC and modern Monte Carlo Tree Search (MCTS) algorithms.

Parallelizing Tree Search with Twice Sequential Monte Carlo

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