Differentiable ES-HyperNEAT: experiments

Code behind the post Differentiable ES-HyperNEAT. Pure Python and NumPy, no GPU, no autodiff library: the CPPN’s backward pass is written out by hand in eshn.py.

File	What it is
`eshn.py`	CPPN (forward and input-gradient), the 2^n-tree substrate search with both split tests, network assembly, the evolution strategy
`tasks.py`	The 3D pursuit task and the four substrate layouts
`run_pursuit.py`	Train one arm for one seed
`scaling.py`	Search cost against substrate dimension, and how often the two split tests disagree
`run_all.py`	The whole protocol, resumable, any OS
`summarise.py`	Turns `results/` into `assets/es-hyperneat/results.json` for the post
`export_swimmers.py`	Exports a few evolved CPPNs (checked against their recorded scores) for the swimming-cell figures

The sampling and gradient arms differ only in complexity() in eshn.py.

Protocol

Fixed before any sampling-vs-gradient comparison was looked at:

pursuit: 120 generations of 32 genomes (16 antithetic pairs), held-out score on 16 fixed chases every 5 generations
- 3D and 2D (map projection) substrates, sampling and gradient tests: seeds 0-7
- 2D “drop z” and 2D ring substrates, sampling only: seeds 0-3
scaling: 10 random CPPNs for each substrate dimension n = 2..7, tree depth 2 (plus depth 3-4 for n = 2, 3)

Running it on Windows

Needs Python 3.10 or newer and git. In PowerShell:

git fetch origin
git checkout es-hyperneat-experiments
cd experiments\es-hyperneat

py -m venv .venv
.\.venv\Scripts\Activate.ps1          # if blocked: Set-ExecutionPolicy -Scope Process Bypass
python -m pip install -r requirements.txt

python run_all.py --smoke             # ~1 minute: checks everything runs, writes to results_smoke\
python run_all.py                     # the real thing

run_all.py uses every core but one and prints a line as each run finishes. Keep the laptop on mains power and stop it sleeping while it runs (powercfg /change standby-timeout-ac 0). If anything interrupts it, run the same command again: finished runs are kept and skipped.

When it finishes it writes assets\es-hyperneat\results.json. Commit the results and push:

cd ..\..
git add experiments/es-hyperneat/results assets/es-hyperneat/results.json
git commit -m "ES-HyperNEAT experiment results"
git push -u origin es-hyperneat-experiments

Simplifications relative to ES-HyperNEAT as published

All of these apply identically to every arm:

The CPPN has a fixed shape (two hidden layers of sin, Gaussian and tanh units, plus a linear skip path) and is trained with an OpenAI-style evolution strategy, not evolved with NEAT.
One tree per network, with complexity averaged over every input and output neuron, rather than one tree per input neuron.
One hidden layer; band pruning is replaced by keeping leaves whose own complexity exceeds a lower threshold.
Two changes were needed before anything learned at all (see the post): 1/sqrt(fan-in) scaling in the substrate network, and shifting the CPPN’s output bias so its average weight starts at zero.