Bayesian Flow Networks for Offline Trajectory Planning

Abstract:Offline reinforcement learning (RL) leverages static datasets to learn decision policies without real-time environment interaction. While recent sequence-modeling approaches rely on continuous diffusion models for trajectory synthesis, applying these methods to discrete planning tasks requires a categorical formulation rather than the standard Gaussian construction. We present BFN-RL, a unified generative modeling framework for offline RL based on Bayesian Flow Networks (BFNs). By iteratively evolving distribution parameters rather than noisy data instances, BFN-RL natively models both discrete and continuous trajectory spaces within a single probabilistic formulation. The categorical planner generates future state sequences, and a learned inverse-dynamics model converts consecutive generated states into actions. Evaluations in discrete planning and continuous control show that BFN-RL can generate effective trajectories across both categorical and continuous state spaces. Our results establish BFNs as a versatile generative foundation for offline trajectory planning across data modalities.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.25163 [cs.LG]

(or arXiv:2608.25163v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.25163 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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