A Survey on Fast Task Adaptation Techniques in Machine Learning

Abstract

Meta-reinforcement learning is a cutting-edge approach that facilitates swift and adaptable responses to new tasks in dynamic environments with significant resource limitations. Utilizing meta-learning principles offers substantial benefits, enabling Meta-RL frameworks to effectively learn from minimal data samples while improving their adaptability and resilience in various domains, including IoT, robotics, autonomous vehicles, and energy management. This paper examines different Meta-Rreinforcement learning approaches and frameworks, such as actor-critic models, Bayesian techniques, attention-driven networks, and adversarial learning methods. The adoption of these frameworks highlights their capabilities.