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arXiv Machine Learning By Florent Tariolle, Florian Yger

DashVMC: Real-Time Discrete World Model Control in Geometry Dash

Read the original on arXiv Machine Learning →

DashVMC is a system that learns a compact, action‑conditioned world model from about two hours of recorded Geometry Dash gameplay. The model is used to train a controller via behavioural cloning and Proximal Policy Optimization entirely in frozen‑model rollouts, achieving longer survival than the initial policy on all tested levels. At deployment, the system runs a 60‑Hz capture‑to‑action loop on a consumer GPU, demonstrating real‑time control despite imperfect long‑horizon fidelity.

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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