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[2602.19837] Meta-Learning and Meta-Reinforcement Learning - Tracing the Path towards DeepMind's Adaptive Agent
Summary
This article surveys meta-learning and meta-reinforcement learning, highlighting their significance in developing DeepMind's Adaptive Agent and the foundational algorithms involved.
Why It Matters
Understanding meta-learning is crucial as it enables AI systems to adapt quickly to new tasks using prior knowledge. This capability is essential for advancing AI towards more generalist approaches, like DeepMind's Adaptive Agent, which can operate effectively across diverse environments.
Key Takeaways
- Meta-learning allows AI to adapt to new tasks with minimal data.
- The paper outlines key algorithms that contributed to DeepMind's Adaptive Agent.
- A formalization of meta-learning concepts is provided for better understanding.
- Transferable knowledge from various tasks enhances AI performance.
- The survey serves as a comprehensive resource for researchers in AI.
Computer Science > Artificial Intelligence arXiv:2602.19837 (cs) [Submitted on 23 Feb 2026] Title:Meta-Learning and Meta-Reinforcement Learning - Tracing the Path towards DeepMind's Adaptive Agent Authors:Björn Hoppmann, Christoph Scholz View a PDF of the paper titled Meta-Learning and Meta-Reinforcement Learning - Tracing the Path towards DeepMind's Adaptive Agent, by Bj\"orn Hoppmann and 1 other authors View PDF HTML (experimental) Abstract: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. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.19837 [cs.AI] (or arXiv:2602.19837v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.19837 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Björn Hoppmann [view email] [v1] Mon, 23 Feb 202...