I am motivated by the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language in service of achieving a goal. I focus on two key components in creating such agents: interactivity and environment grounding, shown to be vital parts of language learning in humans. This work lies primarily at the intersection of Machine Learning, especially Reinforcement Learning, and Natural Language Processing. The core theme of my research involves using knowledge representations such as Knowledge Graphs in conjunction with Graph Neural Networks to create agents that: (1) can understand and generate language within the confines of structured, grounded environments such as Interactive Narratives; and (2) that draw on the principles of Computational Creativity to create engaging stories in structured language environments.”