  Open-world novelty occurs when the rules of an environment can change
abruptly, such as when a game player encounters "house rules". To address
open-world novelty, game playing agents must be able to detect when novelty is
injected, and to quickly adapt to the new rules. We propose a model-based
reinforcement learning approach where game state and rules are represented as
knowledge graphs. The knowledge graph representation of the state and rules
allows novelty to be detected as changes in the knowledge graph, assists with
the training of deep reinforcement learners, and enables imagination-based
re-training where the agent uses the knowledge graph to perform look-ahead.
