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README.md

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+---
+license: unknown
+tags:
+- reinforcement-learning
+- self-play
+- multi-agent
+- ppo
+- checkpoints
+pretty_name: AI Plays Tag — Design C reward-shaping study checkpoints
+---
+
+# AI Plays Tag — Design C checkpoints
+
+Complete training artifacts for the pre-registered **reward shaping ×
+opponent diversity** factorial study in
+[AI-Plays-Tag](https://github.com/kilojoules/AI-Plays-Tag): 195 PPO
+zoo-self-play training runs (plus three 10M-step pilots and the 4
+pre-registered anchor runs), 5M steps each, in a 2D tag environment.
+
+Headline result (confirmatory, pre-registered): reward shaping and zoo
+opponent mixing are **complements** — β_RA = +2.05 log-odds
+[+0.72, +3.45], P(>0) = 0.999 — and dense shaping trades a +30pp mean
+improvement for a 3–4× between-seed "seed lottery". Full study record:
+[`experiments/design_c_results.md`](https://github.com/kilojoules/AI-Plays-Tag/blob/master/experiments/design_c_results.md)
+(pre-registration chain, adversarial-review errata, mechanistic
+follow-up). Evaluation CSVs (gauntlet, anchor panel, MCMC summaries,
+mech-interp lesions) ship in the GitHub repo; this dataset holds the
+**policy checkpoints** the CSVs were computed from.
+
+## Layout
+
+- `runs/<group>__<reward>__A<xx>__seed_<n>.tar.zst` — one archive per
+  training run: final policies (`policy_{seeker,hider}_final.pt`),
+  intermediate checkpoint history (`checkpoints/{seeker,hider}_<upd>.pt`,
+  every ~50 updates, both roles), `train.log`, run metadata.
+- `finals/<group>/<reward>/A<xx>/seed_<n>/policy_*_final.pt` — the final
+  checkpoints loose, for single-policy downloads.
+
+Groups: `grid` (the 2×2 factorial, n=20/cell), `a_sweep` (zoo dose
+A ∈ {0.05, 0.1, 0.25}), `coverage_ablation` / `urgency_ablation` /
+`urgency_only_ablation` (R7 term ablations), `mixed_reward`
+(R7-seeker/R4-hider deconfound cells), `gae_check` (corrected-GAE
+reruns), `hsm_flank` (hider-speed sensitivity), `anchors` (the 4
+pre-registered evaluation anchors), `pilot` (three 10M-step pilots,
+including the SAC HP-BROKEN pair).
+
+## Loading a policy
+
+Policies are 87→128→128→(2·act) tanh MLPs saved as
+`{"pi": state_dict, "vf": state_dict}`. With the GitHub repo checked
+out:
+
+```python
+from experiments.design_c_trajectory_analysis import load_policy
+agent = load_policy("policy_seeker_final.pt", obs_dim=87, act_dim=2)
+```
+
+## Provenance
+
+Trained 2026-05/06 on LUMI and DTU gbar (LSF); rescued to this archive
+after LUMI's retirement. The study was pre-registered (v1–v3, frozen
+before data collection; see the results doc for the deviation ledger),
+survived an adversarial code-and-methods review, and the checkpoint
+histories here support the mechanistic analyses (input-block lesions,
+CKA, discovery-dynamics) described in Part IV of the results doc.
+Licensing follows the GitHub repository.

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