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.

本文介绍了一种基于对象导向表现形式的迁移学习框架，该框架利用人类学习因果模型并将其用于环境的变量之间的迁移。作者将连续优化的结构学习技术应用于对象之间的因果关系的显式学习中，并通过基于因果知识的对象分类将其迁移到目标领域。最后，在强化学习中，作者结合了因果模型和无模型方法，实现对格子世界环境中的对象表现的优化。