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

Abstract:World-model agents are usually evaluated in simulators that can wait for the policy; live games impose the opposite constraint, requiring capture, prediction, and action before the next frame. We present DashVMC, which learns a compact, action-conditioned world model from approximately two hours of recorded Geometry Dash gameplay. To test whether the learned dynamics are actionable, a controller is initialized by behavioural cloning (BC) and refined with Proximal Policy Optimization (PPO) entirely in frozen-model rollouts, without further interaction with the live game. Across three controller seeds, the refined policies survive longer than their BC initializations on all three official levels and a held-out community layout. At deployment, the baseline skips visual generation and sustains a 60-Hz capture-to-action loop on a consumer GPU. Action-conditioned continuations and rollout diagnostics show that the model remains useful for control despite imperfect long-horizon fidelity.

10 pages, 2 figures. Accepted at the NeurIPS 2026 workshop "PTA: From Pretrained Representations to Acting Agents". Project page: this https URL

Machine Learning (cs.LG)

arXiv:2609.40003 [cs.LG]

(or arXiv:2609.40003v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.40003 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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