Valdi: 価値拡散ワールドモデル
Valdi: Value Diffusion World Models
拡散モデルを用いた不確実な未来予測と、オンラインMPCのための高速な潜在計画を両立するワールドモデルを提案。CarRacing環境での予備実験により、単一拡散ステップで決定論的MLPベースラインに匹敵する性能を示した。
著者: Christopher Lindenberg, Kashyap Chitta
分類: cs.LG, cs.AI
原文アブストラクト
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.