  We introduce gym-city, a Reinforcement Learning environment that uses SimCity
1's game engine to simulate an urban environment, wherein agents might seek to
optimize one or a combination of any number of city-wide metrics, on gameboards
of various sizes. We focus on population, and analyze our agents' ability to
generalize to larger map-sizes than those seen during training. The environment
is interactive, allowing a human player to build alongside agents during
training and inference, potentially influencing the course of their learning,
or manually probing and evaluating their performance. To test our agents'
ability to capture distance-agnostic relationships between elements of the
gameboard, we design a minigame within the environment which is, by design,
unsolvable at large enough scales given strictly local strategies. Given the
game engine's extensive use of Cellular Automata, we also train our agents to
"play" Conway's Game of Life -- again optimizing for population -- and examine
their behaviour at multiple scales. To make our models compatible with
variable-scale gameplay, we use Neural Networks with recursive weights and
structure -- fractals to be truncated at different depths, dependent upon the
size of the gameboard.
