Games and Learned Game Engines › Games and Learned Game Engines

Press right in a grid world, and the agent should move one cell unless a boundary stops it. A conventional engine implements that rule directly. A learned engine predicts the next observation from examples of actions and outcomes; it may produce the right picture while getting the rule wrong. That difference matters both to a player who expects a coherent game and to an agent planning through predicted transitions. This chapter uses a small learned grid-world engine to measure the gap between one-step prediction, longer rollouts, and decisions made with the model.

Games are useful testbeds because an evaluator can often reset them, choose actions, and observe rewards. But the game families below pose different problems. A single Atari frame can omit emulator state; a board position in Go exposes the pieces and legal moves; a generated first-person scene must remain responsive and recognizable across actions. No one architecture or metric solves all three.

We will compare Atari, board games, and more open-ended interactive worlds; distinguish models used for agent decisions from worlds rendered for people; and test prediction, rollout error, and model-predictive control in one executable example. A compact evaluation checklist below separates controllability, visual quality, rule fidelity, and decision usefulness; it is not a new benchmark. The chapter builds on Part V: World-Model Architectures and Part VIII: Decision-Centric Research Lineages, before the application domains that follow.