Games and Learned Game Engines › Limitations & Impact

The cited systems establish different accomplishments under different tests. DreamerV3 reports results across more than 150 tasks, not blanket superhuman performance on all 57 Atari games. AlphaZero used known rules for board-game search, and MuZero learned search dynamics. GameNGen demonstrated action-responsive DOOM generation on specified hardware; Genie 2 and Genie 3 reported increasingly long interactive visual worlds. Those claims should be read with their game sets, protocols, hardware, and time horizons attached. They do not imply that one learned engine handles every game or that model-generated experience removes the need for reference-environment tests.

The limitations are equally real, and it is worth being honest about them. Four stand out.

First, evaluation conditions vary even within games. A deterministic board game, Atari with sticky actions, and Minecraft do not share one observability or reward structure. Success in one setting does not establish transfer to physical manipulation or driving, where sensing, action, contact, safety, and data collection impose different constraints. The remaining Part X: Application Domains chapters treat those settings separately.

Second, consistency depends on what is being preserved and for how long. GameNGen reported multi-minute DOOM trajectories, Genie 2 reported worlds up to a minute with most demonstrations shorter, and Genie 3 reported real-time consistency for a few minutes. These are important but not equivalent to arbitrary-length persistence of every object, rule, and reward. Conditioning augmentation, longer context, memory, and re-anchoring incur different costs; no universal doubling or quadrupling factor follows from their names.

Third, optimizing against an imperfect model can favor prediction errors. Uncertainty penalties and short rollouts can reduce particular risks, but a policy still needs evaluation outside the model. The toy MPC result above is a useful reminder: high closed-loop success on a small seeded set does not measure exploitation pressure across other starts, goals, or planners.

Fourth, no single metric covers all intended uses. PSNR compares images, distributional video metrics compare sampled videos, targeted tests count rule violations, and closed-loop return tests whether decisions transfer under a specified protocol. The evaluation checklist earlier keeps these questions separate. Part XI: Evaluation and Understanding develops the broader evaluation framework.

The useful conclusion is narrower than a single progress ladder: learned dynamics can support search, imagined policy updates, or interactive rendering, but each success is conditional on its task and measurement. Before transferring a method to another domain, identify which state, action, reward, latency, and persistence assumptions change.