Meta-Learning of Compositional Task Distributions in Humans and Machines

Modern machine learning systems struggle with sample efficiency and are usually trained with enormous amounts of data for each task. This is in sharp contrast with humans, who often learn with very little data. In recent years, meta-learning, in which one trains on a family of tasks (i.e. a task distribution), has emerged as an approach to improving the sample complexity of machine learning systems and to closing the gap between human and machine learning. However, in this paper, we argue that current meta-learning approaches still differ significantly from human learning. We argue that humans learn over tasks by constructing compositional generative models and using these to generalize, whereas current meta-learning methods are biased toward the use of simpler statistical patterns. To highlight this difference, we construct a new meta-reinforcement learning task with a compositional task distribution. We also introduce a novel approach to constructing a ``null task distribution'' with the same statistical complexity as the compositional distribution but without explicit compositionality. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find that humans do better in the compositional task distribution whereas the agent does better in the non-compositional null task distribution -- despite comparable statistical complexity. This work highlights a particular difference between human learning and current meta-learning models, introduces a task that displays this difference, and paves the way for future work on human-like meta-learning.