Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
39 lines
1.1 KiB
YAML
39 lines
1.1 KiB
YAML
experiment: E5_qd_vs_greedy
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seed: 20260704
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n_replicates: 100
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generations: 400
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# Quality-diversity vs greedy selection (blueprint 2.5-E5). Modest grounding gives a true
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# stationary state (so items can be re-introduced); selection then shapes it. Greedy
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# (directional, fitness-proportional) drives toward the fittest items -> low H; qd (adds a
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# novelty bonus w_i ∝ f_i·p_i^{-alpha}) resists fixation -> higher stationary H. Sweep the
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# novelty exponent alpha. Prediction: qd holds higher stationary H (and tail survival)
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# than greedy at matched grounding.
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truth:
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K: 500
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R: 1
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tail: zipf
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zipf_s: 1.1
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tail_frac: 0.5
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tail_threshold: 1.0e-3
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init: truth
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dynamics:
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n: 200
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teachers: {K_T: 1, rho: 0.0, q: 1.0}
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grounding: {m: 10, policy: proportional} # g ~ 0.048, same for all arms
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selection: {mode: none, novelty_alpha: 0.0}
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remint: {enabled: false, period: null, H_gate: null}
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metrics:
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kl_floor: 1.0e-9
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support_eps: 1.0e-9
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sweep:
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- param: dynamics.selection.mode
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values: [none, greedy, qd]
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- param: dynamics.selection.novelty_alpha
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values: [0.5, 1.0, 2.0]
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output:
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dir: results/figS12_quality_diversity
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