Left action panel revised to show triggers list + Create Token/Auto-Arrange.
CMD DMG badges added to center life-panel score areas (click opens matrix).
Removed renderEnhancedPanels calls that split the battlefield on damage adjust.
Added encoding='utf-8' to all bare open() calls across 17 Python files to
prevent cp1252 crashes on MTG card text with smart quotes/em-dashes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
_discover_jsonl_files now recognises ml-decisions-ds-*.jsonl as its own
"ds" source key instead of falling through to legacy/forge.
DatasetConfig.source_weights gains ds=4.0 as a default so the 8 ds files
(1775 records, ~5.8% raw) are oversampled to ~20% of effective training
tokens, matching their higher signal quality.
CLI gains --ds-weight argument (default 4.0) to override without code changes.
The training_manifest.json produced by PowerShell already encodes weight=4.0
per ds file; this change makes dataset_builder.py honour the same ratio
natively so the manifest and the builder stay in sync.
Implements the three approved Phase 5 architecture decisions:
1. Zone embedding boundary: add explicit documentation in StateDimensions
and StateEncoder._encode_zones making the 6144-dim zero-padded zone
contract visible to future maintainers.
2. SQLite WAL online learning store: new OnlineLearningStore class backed
by WAL-mode SQLite for concurrent read/write on single-machine baseline.
New /api/policy/decide endpoint collects decisions during inference.
Stats endpoint at /api/policy/decide/stats.
3. Forge-only default weights: change ppo_weight default from 0.5 to 0.0
across scope.py, DistillationConfig, DatasetConfig, and CLI defaults.
Add MIXED_MODE_PRESETS dict with forge_only, forge_90_10, forge_80_20
presets. Distill/start endpoint now accepts a "preset" parameter.
27 new tests covering all three decisions (all passing).
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Phase 2 of closed-loop distillation pipeline.
- Scan both ml-decisions-sim-*.jsonl (Forge) and ml-decisions-ppo-*.jsonl (PPO)
- Add source_weights config for configurable sampling ratios
- Add min_reward_threshold filter for PPO data quality
- Tag each sample with source for downstream analysis
- New CLI args: --forge-weight, --ppo-weight, --min-reward
- NPZ output now includes 'sources' array
Replace all print() calls with structured logging using the Python
logging module. This provides:
- Named loggers per subsystem: commander_ai_lab.api, .batch, .sim,
.coach, .deckgen, .collection, .scanner, .ml, .cache, .pplx
- Dual output: console (StreamHandler) + rotating file (RotatingFileHandler)
at logs/commander-ai-lab.log (25 MB x 5 backups = 125 MB max)
- Configurable via env vars: CAL_LOG_DIR, CAL_LOG_MAX_MB, CAL_LOG_BACKUP_COUNT
- --verbose / -v flag for DEBUG-level output
- overnight-run.py: separate rotating log at logs/overnight-run.log (10 MB x 5)
- Sub-module loggers in ml/, scanner/, deck_sources/, src/ all propagate
to the root commander_ai_lab logger
- Stripped redundant [TAG] prefixes from messages (logger name carries context)
- Format: '2026-03-13 14:00:00 INFO [commander_ai_lab.batch] message'
Files changed: lab_api.py, overnight-run.py, and 11 sub-module .py files
176 total print() calls converted (101 in lab_api.py, 76 in sub-modules)
