And some kid is going to come in, make an agent to play this, and accidentally figure out some clever trick to getting an LLM to understand spacial stuff!
This is exactly why "toys" are so critical, especially now.
His key trick: recursive weight-sharing in
fractal convolutional blocks, so each column of the network acts as a
continuous-valued cellular automaton ticking different numbers of times.
The deepest column gets a 33x33 receptive field -- enough to connect power
across an entire 32x32 map in one forward pass.
The agents discovered power-plant + residential pairing, road placement
for density, zone clustering by type, and traffic-avoiding road layouts.
When stuck at local optima, a human player could intervene (deleting power
plants) to force re-exploration -- and the agent would improve its design.
The paper was 2019, before LLMs were doing this kind of thing. Different
paradigm (RL on tile grids vs. LLMs on coordinate text), same hard problem.
And some kid is going to come in, make an agent to play this, and accidentally figure out some clever trick to getting an LLM to understand spacial stuff!
This is exactly why "toys" are so critical, especially now.