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: Few-shot learning is well-studied in the domain of supervised tasks, where the goal is to learn a new function from only a few input/output pairs for that task, using prior data from similar tasks for meta-learning. For example, the goal might be to classify images of a Segway after seeing only one or a few examples of a Segway, with a model that has previously seen many other types of objects. Likewise, in few-shot regression, the goal is to predict the outputs of a continuous-valued function from only a few datapoints sampled from that function, after training on many functions with similar statistical properties. 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 'task'?
Task means what the agent wants to learn. It can be Supervised Learning or Reinforcement Learning, which is represented by initial state, loss function, transition distribution, and episode length H.