Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.

翻译：强大的归纳偏置使人类能够快速学习执行多种任务。尽管元学习是一种赋予神经网络有用归纳偏置的方法，但通过元学习训练的智能体有时会获得与人类截然不同的策略。我们证明，将这些智能体与自然语言任务描述及用于生成此类任务的程序所对应的预测表示进行协同训练，能引导其趋近更接近人类的归纳偏置。人类生成的语言描述与包含新增习得原语的程序归纳模型，均蕴含可压缩描述长度的抽象概念。相较于低抽象程度的对照条件（合成语言描述、不含习得原语的程序归纳），基于这些表示的协同训练使下游元强化学习智能体展现出更贴近人类的行为，表明这些表示所支持的抽象化机制是关键要素。