English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce gym-city, a Reinforcement Learning environment that uses SimCity1's game engine to simulate an urban environment, wherein agents might seek tooptimize one or a combination of any number of city-wide metrics, on gameboardsof various sizes. We focus on population, and analyze our agents' ability togeneralize to larger map-sizes than those seen during training. The environmentis interactive, allowing a human player to build alongside agents duringtraining 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 thegameboard, we design a minigame within the environment which is, by design,unsolvable at large enough scales given strictly local strategies. Given thegame engine's extensive use of Cellular Automata, we also train our agents to"play" Conway's Game of Life -- again optimizing for population -- and examinetheir behaviour at multiple scales. To make our models compatible withvariable-scale gameplay, we use Neural Networks with recursive weights andstructure -- fractals to be truncated at different depths, dependent upon thesize of the gameboard.

Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales