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[2602.18857] VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
Summary
The paper presents VariBASed, a novel approach that integrates variational belief learning and sequential Monte-Carlo planning to enhance data efficiency in deep reinforcement learning.
Why It Matters
This research addresses a critical challenge in reinforcement learning: balancing exploration and exploitation. By improving the efficiency of planning and belief state estimation, VariBASed has the potential to accelerate advancements in AI applications that rely on effective decision-making under uncertainty.
Key Takeaways
- Introduces VariBASed, a method for efficient planning in reinforcement learning.
- Combines variational belief learning with sequential Monte-Carlo techniques.
- Demonstrates improved sample and runtime efficiency compared to existing methods.
- Offers a scalable solution suitable for larger planning budgets.
- Addresses the intractability of belief-state estimation in Bayes-adaptive processes.
Computer Science > Machine Learning arXiv:2602.18857 (cs) [Submitted on 21 Feb 2026] Title:VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning Authors:Joery A. de Vries, Jinke He, Yaniv Oren, Pascal R. van der Vaart, Mathijs M. de Weerdt, Matthijs T. J. Spaan View a PDF of the paper titled VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning, by Joery A. de Vries and 5 other authors View PDF Abstract:Optimally trading-off exploration and exploitation is the holy grail of reinforcement learning as it promises maximal data-efficiency for solving any task. Bayes-optimal agents achieve this, but obtaining the belief-state and performing planning are both typically intractable. Although deep learning methods can greatly help in scaling this computation, existing methods are still costly to train. To accelerate this, this paper proposes a variational framework for learning and planning in Bayes-adaptive Markov decision processes that coalesces variational belief learning, sequential Monte-Carlo planning, and meta-reinforcement learning. In a single-GPU setup, our new method VariBASeD exhibits favorable scaling to larger planning budgets, improving sample- and runtime-efficiency over prior methods. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.18857 [cs.LG] (or arXiv:2602.18857v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.18857 Focus to learn more arX...
