梳理元学习发展脉络，为DeepMind自适应智能体提供理论基础
Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent
- 基于任务的严格形式化框架，统一元学习与元强化学习
- 系统回顾推动DeepMind自适应智能体的关键算法演进
- 适合想理解通用智能体原理的研究者和工程师
人类能高效利用先验知识快速适应新任务，而标准机器学习模型因依赖特定任务训练难以做到这一点。元学习通过从多个任务中习得可迁移的知识，使模型能在少量数据下快速适应新挑战。本文对元学习和元强化学习进行了基于任务的严谨形式化，利用该范式梳理了推动DeepMind自适应智能体诞生的关键算法发展历程，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。
原文摘要 · Abstract (English)
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.
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