You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Learning quickly is a hallmark of human intelligence, whether it involves recognizing objects from a few examples or quickly learning new skillsafter just minutes of experience. Our artificial agents should be able to do the same, learning and adapting quickly from only a few examples, and continuing to adapt as more data becomes available. This kind of fast and flexible learning is challenging, since the agent must integrate its prior experience with a small amount of new information, while avoiding overfitting to the new data. Furthermore, the form of prior experience and new data will depend on the task. As such, for the greatest applicability, the mechanism for learning to learn (or meta-learning) should be general to the task and the form of computation required to complete the task. In this work, we propose a meta-learning algorithm that is general and model-agnostic, in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure. Our focus is on deep neural network models, but we illustrate how our approach can easily handle different architectures and different problem settings, including classification, regression, and policy gradient reinforcement learning, with minimal modification.In meta-learning, the goal of the trained model is to quickly learn a new task from a small amount of new data, and the model istrained by the meta-learner to be able to learn on a large number of different tasks.The key idea underlying our method is totrain the model’s initial parameters such that the model has maximal performance on a new task after the parameters have been updatedthrough one or more gradient steps computed with a small amount of data from that new task.Unlike prior meta-learning methods that learn an update function or learning rule (Schmidhuber, 1987; Bengio et al., 1992; Andrychowicz et al., 2016; Ravi & Larochelle, 2017), our algorithm does not expand the number of learned parameters nor place constraints on the model architecture (e.g. by requiring a recurrent model (Santoro et al., 2016) or a Siamese network (Koch, 2015)), and it can be readily combined with fully connected, convolutional, or recurrent neural networks. It can also be used with a variety of loss functions, including differentiable supervised losses and non-differentiable reinforcement learning objectives.  In reinforcement learning (RL), the goal of few-shot meta-learning is to enable an agent to quickly acquire a policy for a new test task using only a small amount of experience in the test setting. A new task might involve achieving a new goal or succeeding on a previously trained goal in a new environment. For example, an agent might learn to quickly figure out how to navigate mazes so that, when faced with a new maze, it can determine how to reliably reach the exit with only a few samples.In this section, we will discuss how MAML can be applied to meta-learning for RL. All of the meta-learning problems that we consider require some amount of adaptation to new tasks at test-time. When possible, we compare our results to an oracle that receives the identity of the task (which is a problem-dependent representation) as an additional input, as an upper bound on the performance of the model. All of the experiments were performed using TensorFlow (Abadi et al., 2016), which allows for automatic differentiation through the gradient update(s) during meta-learning. The code is available online111Code for the regression and supervised experiments is at github.com/cbfinn/maml and code for the RL experiments is at github.com/cbfinn/maml_rl. The primary contribution of this work is a simple model- and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task.We demonstrate the algorithm on different model types, including fully connected and convolutional networks, and in several distinct domains, including few-shot regression, image classification, and reinforcement learning.Our evaluation shows that our meta-learning algorithm compares favorably to state-of-the-art one-shot learning methods designed specifically for supervised classification, while using fewer parameters, but that it can also be readily applied to regression and can accelerate reinforcement learning in the presence of task variability, substantially outperforming direct pretraining as initialization. The goal of few-shot meta-learningis to train a model that can quickly adapt to a new task using only a few datapoints and training iterations.To accomplish this, the model or learner is trained during a meta-learning phase on a set of tasks, such that the trained model can quickly adapt to new tasks using only a small number of examples or trials.In effect, the meta-learning problem treats entire tasks as training examples.In this section, we formalize this meta-learning problem setting in a general manner, including brief examples of different learning domains.We will discuss two different learning domains in detail in Section 3. We consider a model, denoted f, that maps observations \mathbf{x} to outputs \mathbf{a}.During meta-learning, the modelis trained to be able to adapt to a large or infinite number of tasks.Since we would like to apply our framework to a variety of learning problems, from classification to reinforcement learning, we introduce a generic notion of a learning task below.Formally, each task \mathcal{T}=\{\mathcal{L}(\mathbf{x}_{1},\mathbf{a}_{1},\dots,\mathbf{x}_{H},\mathbf{a}_{H}),q(\mathbf{x}_{1}),q(\mathbf{x}_{t+1}|\mathbf{x}_{t},\mathbf{a}_{t}),H\}consists of a loss function \mathcal{L}, a distribution over initial observations q(\mathbf{x}_{1}), a transition distribution q(\mathbf{x}_{t+1}|\mathbf{x}_{t},\mathbf{a}_{t}), and an episode length H. In i.i.d. supervised learning problems, the length H\!=\!1.The model may generate samples of length H by choosing an output \mathbf{a}_{t} at each time t.The loss \mathcal{L}(\mathbf{x}_{1},\mathbf{a}_{1},\dots,\mathbf{x}_{H},\mathbf{a}_{H})\rightarrow\mathbb{R}, provides task-specific feedback, which might be in the form of a misclassification loss or a cost function in a Markov decision process.

Question: What is the definition of 'meta-learning'?
Meta-learning is a learning method that enables a model to quickly adapt to tasks during learning. Few-shot meta learning means learning a good model within a small number of iterations using only a few training images. Reinforcement learning means that a good policy can be learned using a small number of experiences.