Translations:Diffusion Models Are Real-Time Game Engines/24/en: Difference between revisions

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Our end goal is to have human players interact with our simulation. To that end, the policy <math>\pi</math> as in Section [https://arxiv.org/html/2408.14837v1#S2 2] is that of ''human gameplay''. Since we cannot sample from that directly at scale, we start by approximating it via teaching an automatic agent to play. Unlike a typical RL setup which attempts to maximize game score, our goal is to generate training data which resembles human play, or at least contains enough diverse examples, in a variety of scenarios, to maximize training data efficiency. To that end, we design a simple reward function, which is the only part of our method that is environment-specific (see Appendix [https://arxiv.org/html/2408.14837v1#A1.SS3 A.3]).

Our end goal is to have human players interact with our simulation. To that end, the policy $\pi$ as in Section 2 is that of human gameplay. Since we cannot sample from that directly at scale, we start by approximating it via teaching an automatic agent to play. Unlike a typical RL setup which attempts to maximize game score, our goal is to generate training data which resembles human play, or at least contains enough diverse examples, in a variety of scenarios, to maximize training data efficiency. To that end, we design a simple reward function, which is the only part of our method that is environment-specific (see Appendix A.3).