Meta-learning is a type of machine learning where the model learns to learn from limited data. In traditional reinforcement learning, agents are trained on specific tasks, but meta-reinforcement learning aims to develop an agent that can adapt quickly to new, unseen tasks. This approach has potential applications in areas like robotics and game playing, where agents need to learn from experience and adapt to changing environments. --- Why it matters: This matters because it could enable faster development of intelligent systems that can generalize across multiple tasks, reducing the need for extensive training data and increasing efficiency in real-world applications. Source: https://lilianweng.github.io/posts/2019-06-23-meta-rl/