Memory-Based Meta-Learning
Summary
Memory-based meta-learning is a powerful approach in artificial intelligence that enables the development of sample-efficient strategies capable of adapting to a wide range of tasks within a specific domain. This technique leverages past experiences to create near-optimal predictors and reinforcement learners that can effectively exploit task structure. By recasting memory-based meta-learning within a Bayesian framework, researchers have demonstrated that these strategies achieve near-optimal performance by amortizing Bayes-filtered data, with adaptation implemented through memory dynamics acting as a state-machine of sufficient statistics. Essentially, this approach transforms the complex challenge of probabilistic sequential inference into a more manageable regression problem, making it a valuable tool for creating scalable and adaptable AI agents across broad domains.