Games and Learned Game Engines › Key Parameters
Michael BrenndoerferJuly 25, 20261 min read
World Models HandbookAll chapters Part 10 of 12Application Domains
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The key parameters for the grid-world learned engine are:
- size: Grid dimension. Controls the state space and the number of possible agent and goal positions.
- max_steps: Reference-environment episode budget. It caps collected episodes and recorded-action rollout evaluation; MPC candidate length is set separately by horizon.
- hidden: Width of the MLP trunk. Increasing it raises parameter count and compute per model step; effects on held-out accuracy must be measured.
- lr: Adam learning rate for the world model. An excessively high value can destabilize training; a very low one can slow optimization.
- n_val: Number of held-out transitions. Sets the size of the evaluation split.
- horizon: Random-shooting MPC planning horizon. Longer horizons evaluate more predicted steps and can expose planning to more model error; the amount and direction must be measured on the planner's candidate distribution.
- n_samples: Number of random action sequences evaluated per MPC step. More samples increase search work and may find a higher predicted score; true performance need not improve if reward predictions are wrong.