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arXiv Machine Learning By Ludvig Killingberg, Helge Langseth

Bayesian Flow Networks for Offline Trajectory Planning

Read the original on arXiv Machine Learning →

The paper introduces Bayesian Flow Networks for Offline Trajectory Planning (BFN-RL), a generative modeling framework that unifies discrete and continuous trajectory synthesis for offline reinforcement learning. Unlike prior diffusion models that rely on Gaussian noise, BFN-RL iteratively updates distribution parameters, enabling a categorical planner to produce future state sequences and an inverse-dynamics model to translate these states into actions. Experiments demonstrate that BFN-RL effectively generates trajectories in both discrete planning and continuous control tasks, highlighting its versatility across data modalities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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