This paper introduces Model-Agnostic Meta-Learning (MAML), a meta-learning algorithm that is compatible with any model trained with gradient descent and applicable to various learning tasks, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model that can quickly adapt to new tasks with only a few examples. MAML trains the model's parameters such that a small number of gradient steps with a small amount of data from a new task will produce good generalization performance on that task. This approach enables the model to be easy to fine-tune, allowing for fast adaptation. The paper demonstrates that MAML achieves state-of-the-art performance on few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies. The algorithm is model-agnostic, meaning it can be applied to any model that is trained with gradient descent, including fully connected, convolutional, and recurrent neural networks. It can also be used with various loss functions, including differentiable supervised losses and non-differentiable reinforcement learning objectives. The MAML algorithm is described in detail, including its application to supervised learning and reinforcement learning. In supervised learning, the algorithm is used for regression and classification tasks, while in reinforcement learning, it is used to enable agents to quickly acquire policies for new tasks with only a small amount of experience. The algorithm is evaluated on several tasks, including regression, classification, and reinforcement learning, and is shown to outperform existing methods in terms of performance and efficiency. The paper also discusses related work in meta-learning and few-shot learning, highlighting the advantages of MAML in terms of its model-agnostic nature and its ability to adapt to a wide range of tasks. The experimental results show that MAML can quickly adapt to new tasks with only a few examples, and that it can continue to improve with additional gradient steps. The algorithm is shown to be effective in both supervised and reinforcement learning settings, and is able to outperform other methods in terms of performance and efficiency.This paper introduces Model-Agnostic Meta-Learning (MAML), a meta-learning algorithm that is compatible with any model trained with gradient descent and applicable to various learning tasks, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model that can quickly adapt to new tasks with only a few examples. MAML trains the model's parameters such that a small number of gradient steps with a small amount of data from a new task will produce good generalization performance on that task. This approach enables the model to be easy to fine-tune, allowing for fast adaptation. The paper demonstrates that MAML achieves state-of-the-art performance on few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies. The algorithm is model-agnostic, meaning it can be applied to any model that is trained with gradient descent, including fully connected, convolutional, and recurrent neural networks. It can also be used with various loss functions, including differentiable supervised losses and non-differentiable reinforcement learning objectives. The MAML algorithm is described in detail, including its application to supervised learning and reinforcement learning. In supervised learning, the algorithm is used for regression and classification tasks, while in reinforcement learning, it is used to enable agents to quickly acquire policies for new tasks with only a small amount of experience. The algorithm is evaluated on several tasks, including regression, classification, and reinforcement learning, and is shown to outperform existing methods in terms of performance and efficiency. The paper also discusses related work in meta-learning and few-shot learning, highlighting the advantages of MAML in terms of its model-agnostic nature and its ability to adapt to a wide range of tasks. The experimental results show that MAML can quickly adapt to new tasks with only a few examples, and that it can continue to improve with additional gradient steps. The algorithm is shown to be effective in both supervised and reinforcement learning settings, and is able to outperform other methods in terms of performance and efficiency.