  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.
