Valdi：价值扩散世界模型

摘要

世界模型能夠實現模型預測控制（MPC），但這需要動態預測在線上使用時既足夠快速，又能具備足夠的表達力來呈現不確定的未來。擴散模型提供了一種自然的機制來建模不確定動態，然而其迭代推論過程使其難以應用於低延遲的隱空間規劃。我們透過價值擴散世界模型（Value Diffusion World Models, Valdi）來填補這一缺口，將端到端線上訓練的MPC與隱擴散動態模型相結合。在CarRacing環境的初步實驗中，我們展示出Valdi在訓練和推論時僅使用單一步驟擴散，即可媲美確定性MLP基準的表現。我們的實驗揭示了在此設定下，預測的多模態性與控制效能之間的取捨。程式碼可於 https://github.com/Kit115/ValueDiffusionWorldModels 取得。

English

World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive enough to represent uncertain futures. Diffusion models offer a natural mechanism for modeling uncertain dynamics, yet their iterative inference procedure makes them difficult to use for low-latency latent planning. We bridge this gap with Value Diffusion World Models (Valdi), combining end-to-end online training for MPC with a latent diffusion dynamics model. In preliminary experiments on the CarRacing environment, we show that Valdi, using a single diffusion step at both training and inference, matches a deterministic MLP baseline. Our experiments expose a trade-off between predictive multimodality and control performance in this setup. Code is available at https://github.com/Kit115/ValueDiffusionWorldModels.