  Reinforcement Learning (RL) can enable agents to learn complex tasks.
However, it is difficult to interpret the knowledge and reuse it across tasks.
Inductive biases can address such issues by explicitly providing generic yet
useful decomposition that is otherwise difficult or expensive to learn
implicitly. For example, object-centered approaches decompose a high
dimensional observation into individual objects. Expanding on this, we utilize
an inductive bias for explicit object-centered knowledge separation that
provides further decomposition into semantic representations and dynamics
knowledge. For this, we introduce a semantic module that predicts an objects'
semantic state based on its context. The resulting affordance-like object state
can then be used to enrich perceptual object representations. With a minimal
setup and an environment that enables puzzle-like tasks, we demonstrate the
feasibility and benefits of this approach. Specifically, we compare three
different methods of integrating semantic representations into a model-based RL
architecture. Our experiments show that the degree of explicitness in knowledge
separation correlates with faster learning, better accuracy, better
generalization, and better interpretability.
