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+# Phase 1 — Self-Play Integration Checklist
+
+> **Issue:** #65 — PPO Decision Exporter  
+> **Ref:** `docs/CLOSED-LOOP-DISTILLATION.md`  
+> **Status:** In Progress
+
+---
+
+## Completed
+
+- [x] Create `ml/training/decision_exporter.py` with `DecisionExporter` class
+- [x] `ExporterConfig` dataclass (output_dir, only_wins, model_version, min_episode_steps)
+- [x] `build_decision_record()` — converts PPO step data into Forge-compatible JSONL format
+- [x] `DecisionExporter.begin_episode()` / `record_step()` / `end_episode()` / `flush()` lifecycle
+- [x] Write to `data/results/ml-decisions-ppo-{batch_id}.jsonl`
+- [x] Include PPO metadata fields: `source`, `model_version`, `reward`, `episode_return`
+- [x] Win-only filtering (only export decisions from winning games)
+- [x] Short-episode gating (`min_episode_steps` threshold)
+- [x] `_macro_action_to_forge_type()` mapping (MacroAction enum → Forge action type strings)
+- [x] Export stats tracking (`stats` property)
+
+---
+
+## Remaining — Integrate into `ml/training/self_play.py`
+
+### Import
+
+- [ ] Add import: `from ml.training.decision_exporter import DecisionExporter`
+
+### Modify `run_self_play_episode()` 
+
+- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
+- [ ] After each `trajectory.append(...)` block (3 locations), add:
+  ```python
+  if exporter is not None:
+      exporter.record_step(
+          game_state_snapshot=prev_snapshot,
+          action_idx=action_idx,
+          reward=reward,
+      )
+  ```
+
+### Modify `collect_rollouts()`
+
+- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
+- [ ] Before each episode, call:
+  ```python
+  if exporter is not None:
+      exporter.begin_episode(agent_seat=agent_seat, playstyle=playstyle)
+  ```
+- [ ] Pass `exporter=exporter` to `run_self_play_episode()`
+- [ ] After outcome tracking, call:
+  ```python
+  if exporter is not None:
+      won = trajectory and trajectory[-1]["done"] and trajectory[-1]["reward"] > 0.5
+      ep_return = sum(s["reward"] for s in trajectory)
+      exporter.end_episode(won=won, episode_return=ep_return)
+  ```
+
+### Commit
+
+- [ ] `git add ml/training/self_play.py`
+- [ ] `git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"`
+- [ ] `git push origin main`
+
+---
+
+## Integration Pattern
+
+```
+collect_rollouts(exporter=exporter)
+  └─ for each episode:
+       ├─ exporter.begin_episode(seat, playstyle)
+       ├─ run_self_play_episode(..., exporter=exporter)
+       │    └─ for each step:
+       │         ├─ trajectory.append(step_data)
+       │         └─ exporter.record_step(snapshot, action, reward)
+       └─ exporter.end_episode(won, episode_return)
+  # Caller does: exporter.flush()
+```
+
+---
+
+## Terminal Commands
+
+```bash
+cd commander-ai-lab
+git pull origin main
+# Make edits to ml/training/self_play.py per checklist above
+git add ml/training/self_play.py
+git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"
+git push origin main
+```