Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle to replicate due to their reliance on task-specific training. Meta-learning overcomes this limitation by allowing models to acquire transferable knowledge from various tasks, enabling rapid adaptation to new challenges with minimal data. This survey provides a rigorous, task-based formalization of meta-learning and meta-reinforcement learning and uses that paradigm to chronicle the landmark algorithms that paved the way for DeepMind's Adaptive Agent, consolidating the essential concepts needed to understand the Adaptive Agent and other generalist approaches.

翻译：人类能够高效利用先验知识适应新任务，而标准机器学习模型因依赖任务特定训练而难以复现这种能力。元学习通过使模型从多种任务中获取可迁移知识来克服这一局限，从而能以极少数据快速适应新挑战。本综述基于任务范式对元学习与元强化学习进行严格形式化，并运用该范式系统梳理了为DeepMind自适应智能体奠定基础的关键算法，整合了理解自适应智能体及其他通用方法所需的核心概念。