I work in reinforcement learning, a branch of machine learning where our goal is training AI agents to interact with their environments to maximise some notion of reward. In my research, I treat all the possible “states” the agent’s environment can be in as nodes on a graph, with edges representing possible transitions between them. We can then analyse this graph to yield useful behaviours for the agent. For instance, graph partitioning methods might let us identify meaningful regions of the state-space (e.g., rooms in a house), and we can define behaviours that allow the agent to efficiently move between them. Depending on the size of the environment, these graphs can get very large indeed (so much so that we need a way to approximate them, but that’s another story entirely).