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PTCG AI Battle Challenge — project workspace

Workspace for the Kaggle PTCG AI Battle Challenge (Strategy track, ends 2026-09-14) and its sibling Simulation ladder (pokemon-tcg-ai-battle, the live μ ranking). The project was reset on 2026-06-22 (Session 44) — see RULINGS.md for why.

Read in this order

# File What it is
1 STATE.md Current state + single next action
2 eval/AGENT_CATALOG_FULL.md All 21 ladder submissions decoded (brain × deck × training)
3 RULINGS.md Mindset + evidence + standing rulings
4 ROADMAP.md Now / next / not doing
5 AGENTS.md Operating contract

TASKS.md = build backlog. No other top-level handoff files (R10).

The one-paragraph situation

Imperfect-information POMDP on a TrueSkill ladder. After 21 COMPLETE submissions: only Dragapult official pilot @ 880.9 μ clears 800; best home-grown is SearchScorer × Lucario @ 660.5 μ. Field RL+MCTS v5 regressed to 580.6 μ on the same deck. Every Track B / Snorlax-MCTS path ≤585. Ladder μ sorts; local gates filter.

See eval/AGENT_CATALOG_FULL.md before any train or upload.

Repository map

STATE.md RULINGS.md ARCHITECTURE.md AGENTS.md TASKS.md   # canon
.cursor/SESSION.md                                      # ephemeral session (Cursor hook)
core/        # card/rules/engine/obs model            (scaffold — build first)
field/       # real-field decks + public agents       (scaffold)
episodes/    # Kaggle episode pull/parse/store         (scaffold)
eval/        # the one eval harness + gates            (scaffold)
meta/        # daily meta map                          (scaffold)
discovery/   # scoped deck search                      (scaffold)
agent/       # SHIPPED spine + per-deck agents         (live — do not break)
  lucario_policy.py, lucario_mcts_{runtime,policy}.py, dragapult_agent.py, …
scripts/     # package, gate, train, fetch helpers     (live)
agent_decks/ # real_* + top_mined_* + benchmark/       (live → migrate to field/decks/)
rl_mcts_field/  # local Lucario train outputs (gitignored)
data/        # official rules + card CSVs + engine     (live)
dist/        # packaged submissions                    (live)
tests/       # legality/rules/harness tests            (to populate)
# Reference notebooks at repo root (not under notebooks/):
reinforcement-learning-and-mcts-sample-code.ipynb
a-sample-rule-based-agent-mega-lucario-ex-deck.ipynb

You must provide

  • Kaggle API token at .kaggle/ (gitignored) — used to fetch data and pull episodes.
  • A machine with Python ≥3.11 for the engine and the episode pull (this sandbox is 3.10 and has no Kaggle egress).

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