Apply the same 5-step visual redesign to the LAN client's MTG battlefield
theme while preserving functional game colors (mana, felt table, player
quadrants p1-p4).
- Swap Google Fonts import from Cinzel+Inter to Barlow Condensed + Barlow
- Pure black surfaces (#080808 / 0e / 11 / 16 / 1a)
- Swap primary accent from blue #5b8dd9 to electric violet #8b5cf6
- Square industrial radii (2/4/6/8px)
- Replace all hardcoded 'Cinzel' / 'Inter' with var(--font-display) /
var(--font-body)
- Pure black top bar with violet underglow shadow
- Convert .top-bar-logo "Commander" title from gold to 4-layer violet
neon sign with hover brightening
- Sweep 42 hardcoded rgba(91,141,217,...) blue values downstream to
rgba(139,92,246,...) violet across buttons, zone tabs, stack items,
combat UI, etc.
MTG functional colors (mana W/U/B/R/G, felt table, player quadrants)
remain intact.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Load Barlow + Barlow Condensed from Google Fonts across all 8 HTML
entry points. In global.css introduce --font-display (Barlow Condensed)
alongside --font (Barlow), then wire the display font into every
chrome/heading selector: h1-h6, .page-header, .panel-header, .btn,
.stat-label/.stat-value, .form-label, .badge-status, .data-table
thead, .tag, .nav-brand, .nav-links. Body copy stays in Barlow for
readability.
Also normalize a few hardcoded font-families to design-system vars:
collection.css Cascadia Code stacks and deckgenerator.css Courier New
now use var(--font-mono); canvas rendering in distillation.js and
ai-lab.js swapped from Inter to Barlow so charts match the new type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
LabCli passed relative 'results' to enableMlLogging(), which resolved
to Forge-Repo/forge-gui/results/ because the Java subprocess CWD is
forgeWorkDir. Now uses outputPath's parent directory (absolute) so
JSONL training data goes to commander-ai-lab/results/ where the
Python ML pipeline and Training page expect it.
Forge verbose log uses different verb forms and prefixes than the
patterns expected:
- Turn: 'Turn: Turn 5 (Ai(1)' not 'Turn 5 (Ai(1)'
- Cast: 'cast' (no s) not 'casts', 'Add To Stack:' prefix, no period
- Land: 'played' not 'plays', 'Land:' prefix, card ID suffix '(76)'
- Draw: 'drew' variant, card ID suffix
Updated all four patterns to match both old and new Forge output
formats. This is the final fix for the training data pipeline —
DecisionExtractor will now find cast/land/draw/attack events in
the Forge game log and emit JSONL training snapshots.
Forge's sim mode looks up decks by the Name= field inside the .dck
file metadata header, NOT by filename. For example:
Filename: Counter_Intelligence.dck
Header: Name=Counter Intelligence
Forge expects: 'Counter Intelligence' (with space)
BatchRunner now reads the Name= field from the .dck file and passes
that to Forge. Falls back to filename with underscores→spaces if
the Name= field can't be read.
Forge's sim mode looks up decks by NAME in its own
%APPDATA%/Forge/decks/commander/ directory. Passing a full path like
D:\...\precon-decks\Counterpunch.dck causes 'Could not load deck'.
Now strips the path and .dck extension so Forge sees just the bare
name (e.g. 'Counterpunch') and finds it in its deck folder.
Precon .dck files must be copied to %APPDATA%/Forge/decks/commander/
for Forge to find them.
Frontend: removed .slice(-30) — now shows all lines from backend.
Backend: bumped log line limit from 200 to 2000 so full Forge game
output is captured for debugging.
The condition 'mlLoggingEnabled && !quiet && mlDecisionLogger != null'
silently skipped ML training data extraction whenever quiet mode was
active. DecisionExtractor works with the same log lines regardless of
quiet mode, so the guard was unnecessary and prevented JSONL generation.
Now: mlLoggingEnabled && mlDecisionLogger != null — ML logging runs in
all modes as long as --ml-log is passed.
The Java DecisionLogger was writing files as ml-decisions-{batchId}.jsonl
but the Python dataset_builder.py scans for ml-decisions-sim-*.jsonl to
categorize them as 'forge' source training data.
This one-line fix adds the 'sim-' prefix so the Forge batch heuristic
path now feeds directly into the ML training pipeline:
BatchRunner → DecisionExtractor → DecisionLogger
→ results/ml-decisions-sim-{batchId}.jsonl
→ dataset_builder.py discovers as 'forge' source
→ encode → train
The full chain was already wired (Issue #83 Phase 1 infrastructure),
this naming mismatch was the only thing preventing it from working.
Refs: Issue #83 Phase 1
Ignore static/, logs/, data/rag_chroma/, api/, image_cache/, .claude/,
precon-decks/, and dependency-reduced-pom.xml — all runtime/build
artifacts that were polluting git status. Also stage the one-off
patch scripts (fix_paths.py, patch_eval.py, patch_phase1.py,
forge_evaluator_FIXED.py) and training_manifest.json so they're
tracked alongside the code they support.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
## New File
- PolicyControllerAi.java (840 lines): Extends Forge's PlayerControllerAi
to intercept AI decisions at chooseSpellAbilityToPlay() and route them
through the Python policy server. Falls back to Forge built-in AI on
any failure. Inner PolicyAiController overrides AiController with full
legal action gathering via ComputerUtilAbility.
## Wiring
- GameSession.java: Import + Javadoc for PolicyControllerAi decision flow.
Logs decision pipeline when policy server connects. End-of-game reward
submission via PolicyControllerAi lifecycle.
- BatchRunner.java: New setPolicyServer(url) method. Appends
--policy midrange --policyServer <url> to Forge subprocess command when
policy server URL is configured.
## Path Updates (Forge-Repo rename)
- README.md: Forge link → KoalaTrapLord/Forge-Repo, build path updated
- .env.example: FORGE_JAR_PATH and FORGE_WORKING_DIR → Forge-Repo
- lab_api.py: Auto-detect paths try Forge-Repo first, forge-repo as legacy
## MacroActionExecutor
- Added public isRemovalCard(), isDrawCard(), isRampCard() wrappers so
PolicyControllerAi can consult the same card-role databases.
Refs: Issue #83 Phase 2.4
The phase track (BEGIN · MAIN 1 · COMBAT · END) was losing labels to
ellipsis on narrow top bars, and the primary action buttons were
clipping "NEXT PHASE"/"NEXT TURN" because they had white-space: nowrap
and a fixed height.
.phase-step:
- tighter padding (1px 5px) and gap (2px)
- no letter-spacing, smaller border-radius, flex-shrink: 1 so pills
compress uniformly when the top bar is crowded
- merged duplicate .phase-step.active rule and made it bolder so the
active step still reads well after the shrink
.btn-phase (NEXT PHASE / NEXT TURN):
- white-space: normal + word-break: break-word enable two-line wrap
- height: auto + min-height: 34px lets the label breathe inside
the 44px top bar
- min-width: 64px / max-width: 80px keeps the buttons square-ish
- display: flex centers the wrapped text vertically & horizontally
- align-items: stretch on .top-bar-actions so both buttons match
the tallest sibling's height
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Addresses the three root causes in commander-ai-lab-land-only-bug-analysis.md
plus several collateral fixes discovered along the way.
P0 — Land-per-turn enforcement (engine.py, deepseek_engine.py, turn_manager.py)
* Guard _play_land() against double plays by tracking
_lands_played_this_turn vs _land_plays_this_turn_limit on each player
* Reset per-turn counters at the start of each player's turn
* Ramp cards that grant additional plays can bump the limit
P0 — Legal move pipeline (new legal_move_generator.py, game_state.py)
* New generate_legal_moves() emits the full macro-action set
(play_land, cast_creature, cast_removal, cast_ramp, cast_draw,
cast_commander, attack, block, pass_priority) based on phase,
active player, land-drop state, mana, and commander tax
* get_legal_moves() delegates to the new generator instead of the
pass-only stub that blocked the turn_manager/policy-server path
* apply_move() gains handlers for play_land, cast_spell,
cast_commander, and declare_attacker — spells resolve to
battlefield or graveyard, stats update correctly
P1 — Policy exploration (LearnedPolicy.java, scope.py)
* Default greedy=false so the neural policy samples its
distribution instead of always taking argmax
* Raise ppo_entropy_coeff 0.01 → 0.04 to keep the action
distribution flat during PPO self-play and prevent the
pass/land collapse
Collateral fixes pulled in during the same session:
* lan-client/precon-loader.js: await enrichAllZonesWithScryfall()
so opening-hand cards have typeLine/cmc before the AI's first turn
* lan-client/ui.js: closeZoneModal auto-shuffles the library
whenever the zone modal closes
* routes/game.py: BatchState() was missing total_games/threads,
crashing POST /api/game/start
* routes/deckgen.py: dead code after raise left headers undefined,
breaking the Anthropic API path
All sim/phase/integration tests pass (650+).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add _enrichCardFromCache() in precon-loader.js for synchronous card
enrichment at draw time (typeLine, cmc, oracle roles available before
async Scryfall batch completes)
- Call _enrichCardFromCache in drawCard() so AI has complete card data
on its first turn
- Improve _canAfford in ai-bridge.js: check actual mana pool before
falling back to untapped land count; free spells (cmc 0) always castable
- Fix totalCards field name mismatch in ui.js and engine.js (lab.py
returns totalCards, not card_count/total_cards)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add user-decks/ directory and USER_DECKS_DIR config for exported decks
- Update export-to-sim to write to user-decks/ with static mount in lab_api
- Replace import/paste UI with API-backed "My Decks" dropdown in LAN client
and AI Lab, fetching from GET /api/lab/decks with source grouping
- Fix GET /api/lab/decks to JOIN deck_cards for totalCards count
- Fix deckgenerator: add missing DOM elements (dg-strategy-bar, dg-stats-bar,
dg-card-groups) inside #dg-results, null guards on render functions,
fix setLoading always hiding results
- Replace gpt-oss:20b with claude-opus-4-20250514 across all files
- Add user-decks/ to _BUILTIN_DECK_DIRS in deck_service.py
- Enrich missing type_line from Forge card cache in DB deck path
- Collapse import bar behind <details> toggle in AI Lab
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Static method was referencing bare gameState which is always null.
Now accepts gameState as parameter, and all 5 callers pass it through.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Was reading gameState.battlefield (doesn't exist) causing AI to
receive empty board state. Now reads gameState.battlefieldCards
and matches by ownerIndex.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add bright silver 3D smoky border around commander card zones
- Wrap all 4 life panels in a single bordered container
- Add drag-and-drop from hand zone to battlefield with ghost preview
- Update card placement to center of battlefield, avoiding overlaid zones
- Fix card dimensions in placement calc (110x150 -> 63x84)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move life/cmd-dmg/poison into 3-box life panels as a top-level
overlay (z-index 50) centered at quadrant intersections
- Remove center emblem
- Match battlefield card size to hand cards (63x84px)
- Overlay zone-row and commander-row on battlefield edges to
eliminate dead space and maximize battlefield area
- Restructure poison badges from per-player absolute positioning
into life-panel counters box
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Scale .hand-thumb from 32x45px to 63x84px so cards fill the
hand zone vertically while maintaining MTG card proportions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Forge's sim mode does not honour --policy/--policyServer CLI flags, so
the Python brain was never called during live games. AI only played lands
because Forge ran its built-in AI for every decision.
Fix: when parseForgeLine() detects a Priority event for an AI seat, the
session now immediately calls policyClient.decide() with the current
state snapshot and POSTs the result to /api/policy/override before Forge
reads the next line. This mirrors what handleHumanTurn() already does
for seat 0. The synthetic fallback path is unchanged.
- Add _export_forge_episode() adapter: translates ForgeEpisode transitions
back into DecisionExporter.record_step() calls so Forge-sourced wins flow
into the distillation JSONL pipeline (closes Phase 1 for real games)
- ppo_consumer() now accepts an optional `exporter: DecisionExporter` arg;
begin_episode / record_step / end_episode are called around each episode
as it is dequeued
- run_forge_pipeline() forwards the exporter kwarg down to ppo_consumer and
calls exporter.flush() after the pipeline completes, returning the output
path in the stats dict under "exporter_file"
- Fix snapshots_to_episode(): compute_terminal_reward() was called with
non-existent kwargs `turns=` and `life_remaining=`; compute_intermediate_reward()
was called with non-existent kwargs `life_delta=`, `cards_drawn=`,
`creatures_played=`. Both now use the correct signatures from reward.py
(prev_state/curr_state/action_taken for intermediate; won/draw/timeout for
terminal). Forge snapshots carry `players` arrays so we reconstruct minimal
prev/curr dicts for the intermediate path.
Ref: docs/CLOSED-LOOP-DISTILLATION.md Phase 1
The Forge DecisionExporter writes action={"type":"cast"} without a card
field — action.get("card") and action.get("raw") always returned "",
causing every cast to fall through to the CAST_CREATURE default.
Fix: when card_name is empty, scan the active player's hand against
the removal / draw / ramp / commander name databases and pick the
highest-priority match found. Priority order mirrors cast importance:
removal > ramp > draw > commander > creature (default)
A hand-level match is a reasonable proxy for the decision made:
Forge's AI picks the highest-priority action from available options,
so if the hand contains a removal spell during main phase, the cast
action almost certainly represents that removal spell being played.
Also fixes print_label_distribution: was sorting by dict key (string)
instead of by index, causing the display order to be alphabetical
rather than matching the MacroAction enum order — making cast_creature
appear at position 0 in the sorted output even when it had counts.
Now prints in descending count order with correct enum-keyed lookup.
_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.
Adds a single INFO log line after each successful inference showing:
- chosen action + confidence
- top-5 actions sorted by probability
- turn, phase, vector_source
This lets us diagnose the 'only plays lands' behaviour without a Python
REPL. Remove or downgrade to DEBUG once the training issue is resolved.
Forge sends cmdr_dmg as a dict keyed by opponent seat (e.g. {"0": 14,
"2": 7}). _encode_global read it raw and crashed on `dict / 21.0`.
Fix: resolve cmdr_dmg to a scalar at the read site — sum dict values if
it's a dict, cast to int if it's already a number, default to 0.
This is defensive for all call paths (live Forge IPC, JSONL training,
_build_encoder_snapshot pre-collapsed int) so no upstream change needed.
Both 'Inference error' log lines use identical logger strings, making it
impossible to tell which except block is catching the crash. Also, broad
except blocks were swallowing the full traceback so the real crash site
and cause were hidden.
Changes:
- policy_server.py: logger renamed to 'ml.serving.policy_server';
predict() and predict_batch() error handlers add exc_info=True
- routes/policy.py: logger renamed to 'routes.policy.decide';
_infer_from_vector() error handler adds exc_info=True;
decide() fallback warning adds exc_info=True
The next crash will print the full stack trace, pinpointing the exact
line and call chain causing the TypeError.
The crash `unsupported operand type(s) for /: 'dict' and 'float'` was
firing from _infer_from_vector(), which is the code path taken when
has_precomputed_vector() is True (GameSession 1.1.0+ snapshots with
a precomputed state_vector float[29]).
Unlike PolicyInferenceService.predict() — which was fixed in the
previous commit via _unwrap_logits — this function calls
policy_service.model(state_tensor) directly and immediately uses the
return value in arithmetic (logits / max(temperature, 0.01)), so any
tuple/dict return (DataParallel, torch.compile, PolicyValueNetwork
swap) causes the TypeError.
Fix: import _unwrap_logits from ml.serving.policy_server and apply it
to the raw model output in _infer_from_vector before the temperature
division and argmax calls. All three return shapes are now handled:
- plain tensor → passthrough (normal case, zero overhead)
- tuple/list → raw[0] (PolicyValueNetwork (logits, value))
- dict → first recognised key or first tensor value
If self.model is wrapped (DataParallel, torch.compile, or future
PolicyValueNetwork path), forward() can return a tuple (logits, value)
or even a dict. Add an explicit unwrap immediately after the forward
call in both predict() and predict_batch() so every downstream
temperature/softmax operation always receives a plain tensor.
The root cause of the persistent /api/policy/health 404:
- app.mount("/", StaticFiles(...)) is called at module load time
- StaticFiles acts as a sub-app and consumes matching requests,
returning its own 404 for unknown files rather than falling through
- register_policy_routes() was called in lifespan (runtime), AFTER
the "/" mount was already in the route list — so the policy router
was effectively unreachable
Fix: move route registration to module scope right after all other
app.include_router() calls and BEFORE any app.mount() calls. Only
the model load (_POLICY_SVC.load()) stays in lifespan.
