arXiv Machine Learning June 2, 2026 By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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