Publications

| Sorted by Date | Classified by Publication Type | Classified by Research Category |

Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks

Joery A. De Vries, Jinke He, Mathijs De Weerdt, and Matthijs T. J. Spaan. Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks. Reinforcement Learning Journal, 6:244–275, 2025.

Download

pdf HTML 

Abstract

Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time. From a Bayesian perspective, one can interpret this as performing amortized variational inference on the posterior distribution over training tasks. Among the various meta-reinforcement learning approaches, a common method is to represent this distribution with a point-estimate using a recurrent neural network. We show how one can augment this point estimate to give full distributions through the Laplace approximation, either at the start of, during, or after learning, without modifying the base model architecture. With our approximation, we are able to estimate distribution statistics (e.g., the entropy) of non-Bayesian agents and observe that point-estimate based methods produce overconfident estimators while not satisfying consistency. Furthermore, when comparing our approach to full-distribution based learning of the task posterior, our method performs similarly to variational baselines while having much fewer parameters.

BibTeX Entry

@Article{DeVries25rlc,
  author =       {De Vries, Joery A. and He, Jinke and De Weerdt,
                  Mathijs and Spaan, Matthijs T. J.},
  title =        {Bayesian Meta-Reinforcement Learning with {L}aplace
                  Variational Recurrent Networks},
  journal =      {Reinforcement Learning Journal},
  volume =       6,
  pages =        {244--275},
  year =         2025
}

Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Generated by bib2html.pl (written by Patrick Riley) on Fri Aug 28, 2026 12:56:06 UTC