通过儿童探索和计算模型学习因果过度假设
Learning Causal Overhypotheses through Exploration in Children and Computational Models
摘要：
尽管最近在强化学习（RL）方面取得了进展，但用于探索的RL算法仍然是一个活跃的研究领域。现有方法通常侧重于基于状态的指标，而不考虑环境的潜在因果结构，虽然最近的研究已经开始探索用于因果学习的RL环境，但这些环境主要通过因果推理或归纳而不是探索来利用因果信息。相比之下，人类的孩子--一些最熟练的探索者--已经被证明可以利用因果信息获得巨大的好处。在这项工作中，我们介绍了一种新的RL环境设计与可控的因果结构，这使我们能够评估探索策略，在一个统一的环境中使用的代理和儿童。此外，通过对计算模型和儿童的实验，我们证明了因果环境中的信息增益最优RL探索与儿童在相同环境中的探索之间存在显着差异。最后，我们讨论了这些发现可能会激发新的研究方向，有效的探索和消除歧义的因果结构RL算法。
英文摘要：
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics, which do not consider the underlying causal structures of the environment, and while recent research has begun to explore RL environments for causal learning, these environments primarily leverage causal information through causal inference or induction rather than exploration. In contrast, human children - some of the most proficient explorers - have been shown to use causal information to great benefit. In this work, we introduce a novel RL environment designed with a controllable causal structure, which allows us to evaluate exploration strategies used by both agents and children in a unified environment. In addition, through experimentation on both computation models and children, we demonstrate that there are significant differences between information-gain optimal RL exploration in causal environments and the exploration of children in the same environments. We conclude with a discussion of how these findings may inspire new directions of research into efficient exploration and disambiguation of causal structures for RL algorithms.
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