Introduction to Meta-Reinforcement Learning (Meta-RL) Meta-reinforcement learning (Meta-RL) represents a powerful extension of reinforcement learning (RL) in which the goal is to enable an agent to learn new tasks faster than traditional RL methods would allow. Meta-RL operates under the assumption that multiple related tasks share underlying structures, and by learning these structures, an agent … Continue reading Dissertation on Cutting-Edge Meta-Reinforcement Learning Algorithms: Concepts, Code, and Implementations