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VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning

Joery A. De Vries, Jinke He, Yaniv Oren, Pascal R. Van der Vaart, Mathijs M. De Weerdt, and Matthijs T. J. Spaan. VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning. arXiv:2602.18857, 2026.

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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.

BibTeX Entry

@Misc{DeVries26arxiv,
  author =       {De Vries, Joery A. and He, Jinke and Oren, Yaniv and
                  Van der Vaart, Pascal R. and De Weerdt, Mathijs
                  M. and Spaan, Matthijs T. J.},
  title =        {{VariBASed}: Variational {B}ayes-Adaptive Sequential
                  {M}onte-{C}arlo Planning for Deep Reinforcement
                  Learning},
  howpublished = {arXiv:2602.18857},
  year =         2026
}

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