论文标题
使用自然语言和程序抽象来灌输机器中的人类归纳偏见
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
论文作者
论文摘要
强烈的归纳偏见使人类能够快速学习执行各种任务。尽管元学习是一种具有有用的诱导偏见的方法，但受元学习训练的代理有时可能会从人类中获得截然不同的策略。我们表明，从自然语言任务描述中预测表述和诱导产生此类任务的程序的表示，将它们引导到更类似人类的归纳偏见。人类生成的语言描述和添加新学科的计划归纳模型都包含可以压缩描述长度的抽象概念。对这些表示的共同训练会导致下游元强化学习剂中的人类样行为比较少的抽象控制（合成语言描述，没有学会的基础化的程序诱导）更像，这表明这些表示支持的抽象是关键。
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.
