用世界模型生成虚拟环境，训练出能泛化的智能体。
Imagined Autocurricula
- 用世界模型生成多样化虚拟环境，替代真实数据训练。
- 在新环境中实现强迁移性能，仅用有限数据训练。
- 适合想用小数据训练通用智能体的研究者。
在具身环境中训练智能体通常需要大量数据或精确仿真，但现实中许多场景难以满足。为此，世界模型作为替代方案，利用离线收集的被动数据，可在仿真中生成多样环境用于训练。本文提出 IMAC（Imagined Autocurricula），结合无监督环境设计（UED），自动构建生成环境的训练课程。在一系列程序化生成的挑战性环境中，仅基于较小数据集学习的世界模型，即可在未见环境中实现优异的泛化表现。这为利用大规模基础世界模型训练通用智能体提供了可能。
原文摘要 · Abstract (English)
Training agents to act in embodied environments typically requires vast training data or access to accurate simulation, neither of which exists for many cases in the real world. Instead, world models are emerging as an alternative leveraging offline, passively collected data, they make it possible to generate diverse worlds for training agents in simulation. In this work, we harness world models to generate imagined environments to train robust agents capable of generalizing to novel task variations. One of the challenges in doing this is ensuring the agent trains on useful generated data. We thus propose a novel approach, IMAC (Imagined Autocurricula), leveraging Unsupervised Environment Design (UED), which induces an automatic curriculum over generated worlds. In a series of challenging, procedurally generated environments, we show it is possible to achieve strong transfer performance on held-out environments, having trained only inside a world model learned from a narrower dataset. We believe this opens the path to utilizing larger-scale, foundation world models for generally capable agents.
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