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Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent

arXiv 2602.19837 ↗First posted 23 Feb 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 104 of its 188 references that are in the map. First posted on arXiv 23 Feb 2026.

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References · 104

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38 more
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