Learning transferable knowledge across similar but different settings is a
fundamental component of generalized intelligence. In this paper, we approach
the transfer learning challenge from a causal theory perspective. Our agent is
endowed with two basic yet general theories for transfer learning: (i) a task
shares a common abstract structure that is invariant across domains, and (ii)
the behavior of specific features of the environment remain constant across
domains. We adopt a Bayesian perspective of causal theory induction and use
these theories to transfer knowledge between environments. Given these general
theories, the goal is to train an agent by interactively exploring the problem
space to (i) discover, form, and transfer useful abstract and structural
knowledge, and (ii) induce useful knowledge from the instance-level attributes
observed in the environment. A hierarchy of Bayesian structures is used to
model abstract-level structural causal knowledge, and an instance-level
associative learning scheme learns which specific objects can be used to induce
state changes through interaction. This model-learning scheme is then
integrated with a model-based planner to achieve a task in the OpenLock
environment, a virtual ``escape room'' with a complex hierarchy that requires
agents to reason about an abstract, generalized causal structure. We compare
performances against a set of predominate model-free reinforcement learning(RL)
algorithms. RL agents showed poor ability transferring learned knowledge across
different trials. Whereas the proposed model revealed similar performance
trends as human learners, and more importantly, demonstrated transfer behavior
across trials and learning situations.

采用因果理论的角度来解决迁移学习的问题，在 OpenLock 环境中实现了基于模型和 Bayesian 结构的计划者与模型学习方案，与强化学习相比，该模型表现出更好的迁移表现和类似于人类学习者的表现趋势。