Translations:Diffusion Models Are Real-Time Game Engines/21/zh: Difference between revisions

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GameNGen (pronounced “game engine”) is a generative diffusion model that learns to simulate the game under the settings of Section [https://arxiv.org/html/2408.14837v1#S2 2]. In order to collect training data for this model, with the teacher forcing objective, we first train a separate model to interact with the environment. The two models (agent and generative) are trained in sequence. The entirety of the agent’s actions and observations corpus <math>\mathcal{T}_{agent}</math> during training is maintained and becomes the training dataset for the generative model in a second stage. See Figure [https://arxiv.org/html/2408.14837v1#S3.F3 3].

GameNGen（发音为“游戏引擎”）是一个生成扩散模型，它能够在第2节的设置下学习模拟游戏。为了收集该模型的训练数据，我们首先使用教师强制目标训练一个独立的模型与环境进行交互。这两个模型（代理和生成模型）依次进行训练。在训练过程中，代理的全部行为和观察语料 ${\mathcal {T}}_{agent}$ 被保留下来，并在第二阶段成为生成模型的训练数据集。见图 3。