feat(ui): add tip-icon tooltips to distillation, simulator, coach, deckgenerator pages
- distillation.html: tooltips on all 10 params + ELO Ratings header - simulator.html: tooltips on Opponent, Games, Record logs, API Endpoint, Model, Temp, Timeout - coach.html: tooltips on Power Level, Meta Focus, Budget, Focus Areas, Similar Cards Results - deckgenerator.html: tooltips on Target Bracket, Use my collection, Smart Substitution, AI Model - All pages now share tooltip styles from global.css (no per-page duplication needed)
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@@ -16,7 +16,7 @@
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<div class="page-header" style="display:flex;align-items:center;justify-content:space-between;">
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<div>
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<h1>Closed-Loop Distillation</h1>
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<p>Iterative supervised → PPO → quality gate → merge pipeline</p>
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<p>Iterative supervised → PPO → quality gate → merge pipeline</p>
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</div>
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<button id="btn-refresh" class="btn btn-secondary" onclick="refreshAll()" title="Refresh status">
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<span class="btn-icon" id="refresh-icon">↻</span> Refresh
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@@ -59,43 +59,70 @@
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<div class="train-controls">
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<div class="param-grid form-row">
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<div class="form-group">
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<label class="form-label" for="param-iterations">Max Generations</label>
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<label class="form-label" for="param-iterations">
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Max Generations
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<span class="tip-icon" data-tip="How many distillation cycles to run. Each cycle: supervised train → PPO fine-tune → quality gate. Stops early if convergence is detected.">?</span>
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</label>
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<input type="number" id="param-iterations" class="form-input" value="10" min="1" max="100" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-conv-window">Convergence Window</label>
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<label class="form-label" for="param-conv-window">
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Convergence Window
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<span class="tip-icon" data-tip="Number of recent generations to check for plateau. If ELO gain stays below threshold across this many gens, training stops early to save time.">?</span>
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</label>
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<input type="number" id="param-conv-window" class="form-input" value="3" min="2" max="10" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-sup-epochs">Supervised Epochs</label>
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<label class="form-label" for="param-sup-epochs">
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Supervised Epochs
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<span class="tip-icon" data-tip="Training epochs for the supervised step each generation. Fewer is fine since each cycle trains on incrementally better data. 30 is a solid starting point.">?</span>
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</label>
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<input type="number" id="param-sup-epochs" class="form-input" value="30" min="5" max="200" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-sup-lr">Supervised LR</label>
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<label class="form-label" for="param-sup-lr">
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Supervised LR
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<span class="tip-icon" data-tip="Learning rate for the supervised phase. 0.001 is a solid default. Lower it in later generations if the model is already strong.">?</span>
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</label>
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<input type="number" id="param-sup-lr" class="form-input" value="0.001" step="0.0001" min="0.00001" max="0.1" />
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</div>
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</div>
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<div class="param-grid form-row">
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<div class="form-group">
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<label class="form-label" for="param-ppo-iters">PPO Iterations</label>
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<label class="form-label" for="param-ppo-iters">
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PPO Iterations
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<span class="tip-icon" data-tip="Number of PPO update steps per generation. Each step samples game episodes, computes advantages, and updates policy weights.">?</span>
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</label>
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<input type="number" id="param-ppo-iters" class="form-input" value="50" min="10" max="500" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-ppo-episodes">PPO Episodes/Iter</label>
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<label class="form-label" for="param-ppo-episodes">
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PPO Episodes/Iter
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<span class="tip-icon" data-tip="Game simulations per PPO update step. More episodes = more stable gradients but slower iterations. 64 balances speed and stability well.">?</span>
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</label>
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<input type="number" id="param-ppo-episodes" class="form-input" value="64" min="8" max="512" step="8" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-ppo-lr">PPO Learning Rate</label>
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<label class="form-label" for="param-ppo-lr">
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PPO Learning Rate
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<span class="tip-icon" data-tip="PPO step size. Keep this lower than the supervised LR. 0.0003 is the standard RL default and works well here.">?</span>
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</label>
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<input type="number" id="param-ppo-lr" class="form-input" value="0.0003" step="0.0001" min="0.00001" max="0.01" />
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</div>
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<div class="form-group">
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<label class="form-label" for="param-min-wr">Min Win Rate Gate</label>
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<label class="form-label" for="param-min-wr">
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Min Win Rate Gate
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<span class="tip-icon" data-tip="Quality gate threshold. A generation must beat the heuristic opponent at this win rate to be accepted and merged into the next cycle. 0.30 is a lenient starting gate.">?</span>
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</label>
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<input type="number" id="param-min-wr" class="form-input" value="0.30" step="0.05" min="0.1" max="0.9" />
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</div>
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</div>
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<div class="param-grid form-row distill-bottom-params">
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<div class="form-group">
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<label class="form-label" for="param-opponent">Opponent</label>
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<label class="form-label" for="param-opponent">
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Opponent
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<span class="tip-icon" data-tip="Who the agent trains against during PPO. Heuristic is recommended for a consistent challenge. Self-play can become unstable without a strong supervised baseline first.">?</span>
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</label>
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<select id="param-opponent" class="form-select">
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<option value="heuristic">Heuristic</option>
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<option value="random">Random</option>
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@@ -103,7 +130,10 @@
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</select>
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</div>
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<div class="form-group">
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<label class="form-label" for="param-playstyle">Playstyle</label>
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<label class="form-label" for="param-playstyle">
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Playstyle
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<span class="tip-icon" data-tip="Deck archetype used for simulations in this distillation run. Midrange is the most balanced test bed. Match this to the archetype you want the model to master.">?</span>
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</label>
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<select id="param-playstyle" class="form-select">
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<option value="midrange">Midrange</option>
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<option value="aggro">Aggro</option>
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@@ -164,7 +194,9 @@
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<!-- ELO Rating Chart -->
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<section class="panel" id="elo-panel">
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<div class="panel-header elo-panel-header">
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<h2>ELO Ratings</h2>
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<h2>ELO Ratings
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<span class="tip-icon tip-below" data-tip="ELO scores measure relative strength. Baseline is 1200. Scores above that mean the model wins more than it loses. Tournament runs the top 10 most recent checkpoints head-to-head.">?</span>
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</h2>
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<button id="btn-run-elo" class="btn btn-sm btn-secondary" onclick="runEloTournament()">
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<span class="btn-icon">🏆</span> Run Tournament
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</button>
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