Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information.   In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks.   On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins. The ability to learn quickly is a key characteristic that distinguishes human intelligence from its artificial counterpart. Humans effectively utilize prior knowledge and experiences to learn new skills quickly. However, artificial learners trained with traditional supervised-learning or reinforcementlearning methods generally perform poorly when only a small amount of data is available or when they need to adapt to a changing task. Meta-learning seeks to resolve this deficiency by broadening the learner's scope to a distribution of related tasks. Rather than training the learner on a single task (with the goal of generalizing to unseen samples from a similar data distribution) a meta-learner is trained on a distribution of similar tasks, with the goal of learning a strategy that generalizes to related but unseen tasks from a similar task distribution. Traditionally, a successful learner discovers a rule that generalizes across data points, while a successful meta-learner learns an algorithm that generalizes across tasks. Many recently-proposed meta-learning methods demonstrate improved performance at the expense of being hand-designed at either the architectural or algorithmic level. Some have been engineered with a particular application in mind, while others have aspects of a particular high-level strategy already built into them. However, the optimal strategy for an arbitrary range of tasks may not be obvious to the humans designing a meta-learner, in which case the meta-learner should have the flexibility to learn the best way to solve the tasks it is presented with. Such a meta-learner would need to have an expressive, versatile model architecture, in order to learn a range of strategies in a variety of domains. Meta-learning can be formalized as a sequence-to-sequence problem; in existing approaches that adopt this view, the bottleneck is in the meta-learner's ability to internalize and refer to past experience. Thus, we propose a class of model architectures that addresses this shortcoming: we combine temporal convolutions, which enable the meta-learner to aggregate contextual information from past experience, with causal attention, which allow it to pinpoint specific pieces of information within that context. We evaluate this Simple Neural AttenIve Learner (SNAIL) on several heavily-benchmarked meta-learning tasks, including the Omniglot and mini-Imagenet datasets in supervised learning, and multi-armed bandits, tabular Markov Decision processes (MDPs), visual navigation, and continuous control in reinforcement learning. In all domains, SNAIL achieves state-of-the-art performance by significant margins, outperforming methods that are domain-specific or rely on built-in algorithmic priors. We presented a simple and generic class of architectures for meta-learning, motivated by the need for a meta-learner to quickly incorporate and refer to past experience. Our simple neural attentive learner (SNAIL) utilizes a novel combination of temporal convolutions and causal attention, two building blocks of sequence-to-sequence models that have complementary strengths and weaknesses. We demonstrate that SNAIL achieves state-of-the-art performance by significant margins on all of the most-widely benchmarked meta-learning tasks in both supervised and reinforcement learning, without relying on any application-specific architectural components or algorithmic priors.Although we designed SNAIL with meta-learning in mind, it would likely excel at other sequence-tosequence tasks, such as language modeling or translation; we plan to explore this in future work.Another interesting idea would be to train an meta-learner that can attend over its entire lifetime of experience (rather than only a few recent episodes, as in this work). An agent with this lifelong memory could learn faster and generalize better; however, to keep the computational requirements practical, it would also need to learn how to decide what experiences are worth remembering.
a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks
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