Headless fleet images may lack the [extra] deps; fall back to progress_bar=False
instead of crashing model.learn(). Surfaced by the clan-conditioning smoke (the
clan wiring itself verified: CP_LEARNER_CLAN=blackhammer -> clan_index=2 in the obs).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The wiring for per-clan trained AI. Each training episode samples a clan, stamps it
on the LEARNER slot so the obs one-hots it, and scales the SHAPING rewards by that
clan's overlay (terminal win/loss stay universal):
- player_api_main.gd: CP_LEARNER_CLAN stamps the learner slot's clan via
set_player_personality_json -> PlayerState.clan_id -> PlayerView.clan_index ->
obs clan one-hot. (Previously only non-learner slots got a clan.)
- reward_overlays.json: per-clan group multipliers (combat/expansion/production/
economy/tech) derived from ai_personalities.json strategic_axes, normalized per
clan to mean 1.0 (no fairness confound). Archetypes emerge: blackhammer combat 1.5,
goldvein economy 1.64, deepforge expansion 0.42.
- magic_civ_env.py: samples the clan per episode (seeded), passes CP_LEARNER_CLAN,
scales the 8 shaping reward terms by self._ov(group).
- harness_client.py: HarnessConfig.learner_clan -> CP_LEARNER_CLAN.
- train.py: --clan ('' generalist | 'all' samples every clan | comma list).
Local checks: py_compile clean; overlays cover all 6 clans. Next: fleet smoke
(clan_index in the learner view + a tiny training run) before scaling out.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
