Meta-Learning and Meta-Reinforcement Learning - Tracing the Path towards DeepMind's Adaptive Agent
It provides a comprehensive overview for researchers interested in generalist AI agents, though it is incremental as a survey consolidating existing work.
This survey formalizes meta-learning and meta-reinforcement learning to trace the development of algorithms leading to DeepMind's Adaptive Agent, enabling models to adapt quickly to new tasks with minimal data by leveraging prior knowledge.
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.