Meta-Learning for Autonomous Ai Agents: Enabling Self-Improvement Beyond Training Data

Abstract

This review explores the role of meta-learning in advancing autonomous AI agents capable of self-improvement beyond fixed training data. It examines core algorithms, theoretical models, and real-world applications to provide a comprehensive understanding of how meta-learning enables generalization and adaptation across tasks. The paper systematically categorizes meta-learning techniques into optimization-based, metric-based, model-based, and Bayesian approaches. It introduces formal mathematical models to define self-improvement using performance operators, convergence analysis, and regret minimization. Empirical benchmarks such as MiniImageNet, Omniglot, and MetaWorld are reviewed to illustrate performance trends. Meta-learning systems achieve high adaptability, with few-shot classification accuracies reaching 65–95% on standard benchmarks and up to 60% gains in sample efficiency for reinforcement learning agents. These systems facilitate rapid task adaptation, continual learning, and feedback-driven self-regulation, laying the foundation for strong autonomy in AI. The integration of meta-learners into robotics, NLP, vision, and human-in-the-loop systems demonstrates their potential for real-time, resilient intelligence in real-world environments. This review bridges theoretical insights with applied meta-learning, highlighting current limitations such as catastrophic forgetting and offering directions toward scalable, self-evolving AI agents.