标题： 探索驱动的生成式交互环境 显示英文标题

标题： Exploration-Driven Generative Interactive Environments

摘要： 现代世界模型需要耗费大量时间和成本来收集带有操作演示的大型视频数据集。为了简化训练，我们专注于使用多个虚拟环境，以获取廉价且自动收集的交互数据。最近的多环境世界模型 Genie 展示了共享行为的许多环境的模拟能力。然而，训练他们的模型仍需要昂贵的操作演示。因此，我们提出了一种仅使用虚拟环境中随机代理的训练框架。虽然以这种方式训练的模型表现出良好的控制能力，但其受到随机探索可能性的限制。为了解决这一局限性，我们提出了 AutoExplore Agent——一种完全依赖于世界模型不确定性的探索代理，能够提供多样化的数据，从而学习到最佳行为。我们的代理完全独立于特定环境的奖励，因此可以轻松适应新环境。通过这种方法，预训练的多环境模型可以快速适应新环境，提高视频保真度和可控性。为了获得用于预训练的自动大规模交互数据集，我们将具有相似行为和控制的环境分组。为此，我们标注了 974 个虚拟环境的行为和控制——我们将其称为 RetroAct 数据集。为了构建我们的模型，我们首先创建了 Genie 的开源实现 GenieRedux，并在我们的版本 GenieRedux-G 中应用了增强和适配。我们的代码和数据可在 https://github.com/insait-institute/GenieRedux 获取。 显示英文摘要

摘要： Modern world models require costly and time-consuming collection of large video datasets with action demonstrations by people or by environment-specific agents. To simplify training, we focus on using many virtual environments for inexpensive, automatically collected interaction data. Genie, a recent multi-environment world model, demonstrates simulation abilities of many environments with shared behavior. Unfortunately, training their model requires expensive demonstrations. Therefore, we propose a training framework merely using a random agent in virtual environments. While the model trained in this manner exhibits good controls, it is limited by the random exploration possibilities. To address this limitation, we propose AutoExplore Agent - an exploration agent that entirely relies on the uncertainty of the world model, delivering diverse data from which it can learn the best. Our agent is fully independent of environment-specific rewards and thus adapts easily to new environments. With this approach, the pretrained multi-environment model can quickly adapt to new environments achieving video fidelity and controllability improvement. In order to obtain automatically large-scale interaction datasets for pretraining, we group environments with similar behavior and controls. To this end, we annotate the behavior and controls of 974 virtual environments - a dataset that we name RetroAct. For building our model, we first create an open implementation of Genie - GenieRedux and apply enhancements and adaptations in our version GenieRedux-G. Our code and data are available at https://github.com/insait-institute/GenieRedux.