论文标题
结构映射，可转移因果模型
Structure Mapping for Transferability of Causal Models
论文作者
论文摘要
人类学习因果模型，并不断利用它们在类似环境之间转移知识。我们使用这种直觉来设计一个使用面向对象的表示的转移学习框架，以了解对象之间的因果关系。学习的因果动力学模型可用于在对象之间具有可交换感知特征但具有相同基本因果动力学的环境变体之间传输。我们对结构学习技术进行了连续优化，以明确地学习互动环境中的作用和影响，并通过基于因果知识对对象进行分类，转移到目标域。我们通过将基于因果模型的方法与无模型方法相结合的增强学习中的方法来证明我们方法在环境环境中的优势。
Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects but with the same underlying causal dynamics. We adapt continuous optimization for structure learning techniques to explicitly learn the cause and effects of the actions in an interactive environment and transfer to the target domain by categorization of the objects based on causal knowledge. We demonstrate the advantages of our approach in a gridworld setting by combining causal model-based approach with model-free approach in reinforcement learning.
