The three split-combat helpers were accidentally defined as nested
functions inside _resolve_combat(), making them unreachable as
GameEngine.assign_attackers() etc. from tests and turn_manager.
Move them out to class-level methods with correct 4-space indentation.
_resolve_combat() now remains as the backward-compatible atomic path for
run_n(); the split methods are available for turn_manager priority
windows and direct testing.
Replaces synthetic stub loop with a live Forge process:
- Spawns Forge with --policy + --policyServer flags so Forge calls
back to /api/policy/decide at each AI decision point
- Reads Forge stdout line-by-line and parses zone/phase/life updates
via the same regex patterns used in BatchRunner
- Syncs full board state (battlefield, hand, graveyard, exile,
command zone, stack) on every Forge state-change line
- Routes human input from humanActionQueue via a side-channel
HTTP POST to /api/policy/override on the policy server
- Handles activePlayer + priorityPlayer correctly for 3-4 player pods
- Falls back to synthetic stub loop if Forge subprocess fails to start
- Watchdog thread kills stalled Forge process after 10 min
Adds a buildStateVector(int seat) method to GameSession that produces
a float[29] global scalar vector matching STATE_DIMS layout in
ml/config/scope.py:
Per-player block (14 features × 2 players = 28):
[0] life_total / 40.0
[1] commander_damage / 21.0 (stub 0.0 until real Forge tracks it)
[2] mana_available / 20.0 (stub 0.0 until mana pool wired)
[3] commander_tax / 10.0
[4] cards_in_hand / 15.0
[5] cards_in_graveyard / 100.0
[6] creatures_on_battlefield / 30.0
[7] total_power_on_board / 100.0 (stub = creature count until P/T data)
[8] is_active_player (0 or 1)
[9] phase one-hot[0] main1
[10] phase one-hot[1] combat
[11] phase one-hot[2] main2
[12] phase one-hot[3] end
[13] (padding / reserved)
Global:
[28] turn_number / max_turns (25)
buildStateSnapshot() now includes "state_vector": float[] so that
PolicyClient.decide(JsonObject) sends a machine-readable vector the
Python /api/policy/decide endpoint can pass directly to the encoder
without re-parsing the nested human-readable fields.
Stub fields (mana_available, commander_damage, total_power_on_board)
are zero until real Forge board state is wired in Step 4.
- _get_cards_folder() now tries both forge-gui/res/cardsfolder and res/cardsfolder
so FORGE_DIR can point to either the repo root or the forge-gui subdirectory
- Add reset_forge_cache() to allow hot-reloading without server restart
The previous backfill operated on seen_names dict which silently
ignored basics already present (dict keys are unique). Now backfills
directly on card_entries list after Scryfall fetch, incrementing
quantity on existing basics or appending new CardEntry objects.
Distributes shortfall evenly across commander color identity basics.
Closes#112
- Add sim_insights.py to translate learned_weights.json into LLM prompt text
- Add sim_context param to suggest_cards() and enforce_deck_ratios() in ollama_client.py
- Wire get_sim_insights() into build_deck pipeline (Steps 4 & 6)
- Fully backwards compatible — no-op until real sim data exists
enforce_deck_ratios can return the same card name under multiple
categories (e.g. Teferi's Protection in both protection + synergy).
all_card_names had no cross-category dedup, so get_cards_by_names
returned duplicate CardEntry objects that then failed the singleton
validator in CommanderDeck.
Fix: build all_card_names with dict.fromkeys() across all categories
so each name appears exactly once. Also deduplicate after Scryfall
lookup by name as a second safety net.
GameSession.java references these APIs which were missing from
PolicyClient, causing 3 compilation errors. Added:
- getBaseUrl() getter
- PolicyDecision record (action, confidence)
- decide(String, int) overload that delegates to decide(JsonObject)
DecisionExtractor requires verbose Forge output to reconstruct board
states. When quiet=true, Forge runs with -q which strips the verbose
log lines, causing 0 decisions to be extracted. This change passes
quiet=false to BatchRunner when mlLogEnabled is true.
* feat(83): add PolicyClient for live Forge-to-policy-server IPC (#83)
Phase 2.1: Bridge between Forge decision points and Python policy
server for online training. Calls /api/policy/decide endpoint,
collects (state, action, reward) tuples in real-time, and flushes
them to the server for online PPO updates.
Falls back to Forge built-in AI if server is unavailable.
* feat(83): add /api/policy/* routes for live Forge IPC (#83)
Phase 2.2: Policy decision routes for real-time Forge IPC.
- POST /api/policy/decide — full game state inference with log_prob/value
- POST /api/policy/collect — online learning tuple batch collection
- POST /api/policy/reward — end-of-game reward propagation
- GET /api/policy/health — readiness check for Java PolicyClient
- GET /api/policy/stats — session statistics
Includes Forge phase mapping and PlayerZoneState with full zone contents.
* feat(83): integrate PolicyClient into GameSession for bidirectional sync (#83)
The hardcoded 192.168.0.240 IP was a developer-specific LAN address
that breaks out-of-the-box for anyone running Ollama locally or via
WSL2 on Windows. Switch every default and docstring reference to
localhost so the project works without manual configuration.
Files changed:
- coach/config.py: LLM_URL and DECK_GEN_BASE_URL defaults
- ui/simulator.html: API endpoint input value and placeholder
- lab_api.py: remove LAN IP from default CORS origins
- routes/coach.py: warning message text
- src/commander_ai_lab/sim/deepseek_brain.py: docstrings
- src/commander_ai_lab/sim/deepseek_engine.py: docstring example
- src/commander_ai_lab/sim/validator_brain.py: docstring example
Co-authored-by: Claude Code <noreply@anthropic.com>
`last_err = None` was at module-level indentation (4 spaces) instead of
method-body indentation (8 spaces), causing an IndentationError on import.
Co-authored-by: Claude Agent <claude-agent@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
max_retries was configured but never used — choose_action() made
exactly one attempt. Now wraps _call_llm() in a retry loop.
Bumped max_retries default to 2 and max_tokens to 4096 (R1 needs
room for <think> blocks + JSON). Fixes issue #95 item 3.
_parse_action() used r'\{[^{}]*\}' regex which fails on nested
JSON (e.g. oracle text with braces). Now uses text.find/rfind to
extract the outermost { } block, handling nested structures.
Fixes issue #95 item 2.
check_connection() was auto-overwriting self.config.model with the
first model returned by /v1/models, causing deepseek-r1:8b to be
replaced by deepseek-r1:14b (or whatever Ollama listed first).
Now logs available models without changing the configured model.
Fixes issue #95 item 1.
* feat(issue-80): Phase 1 — add services/forge_card_loader.py
* feat(issue-80): Phase 2 — add 5 Forge fields to Card dataclass
* feat(issue-80): Phase 3+4 — wire Forge into enrich_card/score_card
* feat(issue-80): Phase 5 — add tests/sim/test_forge_enrichment.py
* fix(issue-80): resolve PR #81 blocking issues — import paths, indentation, partial Forge fallback
- Move forge_card_loader.py from services/ into src/commander_ai_lab/sim/
so the import in rules.py uses a proper intra-package path instead of
crossing package boundaries via services.forge_card_loader
- Fix indentation of Forge enrichment comment in models.py (8→4 spaces)
- Fix enrich_card() early return on partial Forge data: re-check
has_real_data after _apply_forge_data so cards with incomplete Forge
entries still fall through to known-cards / default heuristics
- Fix flawed test assertion in test_etb_via_forge_mode_equals_text_detection:
oracle text "draw a card" was adding a +3 spell_draw bonus, making the
score comparison invalid; replaced with side-effect-free ETB text
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Code <claude-code@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Refactor the headless engine to walk through all 12 MTG turn phases in
order (untap → upkeep → draw → main1 → begin_combat → declare_attackers
→ declare_blockers → combat_damage → end_combat → main2 → end_step →
cleanup) instead of the previous flat per-player block.
Key changes:
- Add Phase str enum and PHASE_ORDER/SORCERY_PHASES constants to models
- Track current_phase and active_player_index on SimState
- Sync sim.turn with the loop turn counter (fixes latent summoning-
sickness bug where turn_played was always compared against 0)
- Add second main phase (main2) with shared 2-spell budget across
main1 + main2
- _play_spells now accepts max_spells param and returns count played
- Unify turn_manager.py PHASES/ACTION_PHASES/RESPONSE_PHASES to derive
from the canonical Phase enum in models.py
- 33 regression tests covering phase enum, phase progression, spell
budget, game log structure, backward compat, and turn manager
constant consistency
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add London mulligan rule to the headless game engine:
- GameEngine now accepts mulligan_rule param ("london" default, "none" for legacy)
- AI heuristic: mulligan 0-1 land or 6-7 land hands; relax after 2+ mulligans
- After keeping, bottom N lowest-scored cards (London mulligan rule)
- Cap at MAX_MULLIGANS=4 to prevent infinite loops on degenerate decks
- Track mulligans and opening_hand_lands in PlayerStats
- Serialize both fields in to_dict() for batch-result schema compatibility
- 28 targeted regression tests covering unit helpers, integration,
N-player, commander interaction, serialization, and legacy mode
Co-authored-by: Claude Agent <claude-agent@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add centralized deal_damage() method to GameEngine that handles life
reduction, stat tracking, commander damage (both per-seat and per-card),
and elimination checks. Route both burn spells and combat damage through
this unified path, building on the dual-dict commander damage model
merged in PR #89.
- Add is_direct_damage / direct_damage_amount fields to Card
- Add burn spell detection via known-card table + oracle regex fallback
- Route burn spells in _play_spells() through deal_damage()
- Refactor _resolve_combat() to route per-attacker damage through deal_damage()
- Add 38 regression tests covering deal_damage(), burn detection, spell routing,
combat routing, and full-game integration
Co-authored-by: Claude Code <noreply@anthropic.com>
Track commander damage at combat-damage time by checking `is_commander`
on each attacking creature. Damage from commander creatures is now
recorded in both a per-seat aggregate (`commander_damage_received`) and
a per-card breakdown (`commander_damage_by_card`), enabling correct
21-damage lethal checks for partner/multi-commander games.
Changes:
- engine._resolve_combat: identify commander attackers, accumulate their
damage (including trample overflow and double strike), write to both
tracking dicts, and check commander-damage elimination after combat.
- models.Player: add commander_damage_received, commander_damage_by_card,
and is_dead_to_commander_damage() so the headless engine can track
commander damage without the full CommanderGameState wrapper.
- game_state.CommanderPlayer: replace the separate
commander_damage_received field with a property delegating to
base.Player to avoid split-brain state between engine and wrapper.
- game_state.CommanderGameState.deal_commander_damage: accept optional
commander_name for per-card tracking.
- 22 new regression tests covering unblocked, blocked, trample, double
strike, lifelink, partner, cumulative, and lethal scenarios.
Partial fix for Issue #86 item 2.
Co-authored-by: Claude <noreply@anthropic.com>
Previously _resolve_combat() used a binary power-vs-toughness kill check:
creatures only died if a single source had power >= their toughness in one
swing. This meant a 2/2 wouldn't die to two 1/1 tokens blocking it, and
repeated smaller hits across combats never accumulated.
Changes:
- models.py: add damage_marked field to Card (default 0)
- engine.py: accumulate damage via damage_marked += power instead of
binary a_pow >= toughness checks in _resolve_combat()
- engine.py: clear damage_marked = 0 in untap phase (MTG rule 514.2)
This enables multi-blocker lethality, incremental chip damage within a
combat step, and proper creature death-by-accumulated-damage interactions.
- Add ml_log param, ml_decisions list, flush_ml_decisions() to GameEngine
- Capture ML snapshots at each player turn in run_n()
- Stamp game_outcome (win/loss per seat) on all decisions
- Add _capture_ml_snapshot() matching DeepSeekGameEngine format
- Add game_id and archetype params to run_n() for ML tracking
- Import logging, uuid; add logger
Add "validation" key to _log_decision() entry dict in deepseek_brain.py.
Captures result.get("validation") (None if validator disabled)
for R1-14B output preservation and future analysis.
KoalaTrapLord