582 lines
47 KiB
XML
582 lines
47 KiB
XML
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<text x="700" y="32" text-anchor="middle" font-size="21" font-weight="bold">SAC-LSTM campaign dashboard - live run (current only)</text>
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<text x="700" y="56" text-anchor="middle" font-size="12" fill="#555">generated 2026-08-24 10:18:35 - auto-reloads every 60 s (open this file in Chrome)</text>
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<text x="70" y="100" font-size="15" font-weight="bold">Test matches - win % vs opponents</text>
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<text x="70" y="115" font-size="11" fill="#555">raw dots = single test matches, thick = rolling-mean-10</text>
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<text x="70" y="133" font-size="12" font-style="italic" fill="#555">What: How often the bot wins against each opponent in test battles.</text>
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<text x="70" y="149" font-size="12" font-style="italic" fill="#555">Axes: X = training progress (match number); Y = win rate, 0-100%.</text>
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<text x="70" y="165" font-size="12" font-style="italic" fill="#555">Better: higher is better.</text>
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<text x="383" y="817" text-anchor="middle" font-size="12">test match number (each opponent)</text>
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<text x="16" y="484" text-anchor="middle" font-size="12" transform="rotate(-90 16 484)">win rate (%)</text>
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<text x="116" y="840" font-size="12">Corners - 35 evals</text>
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<text x="116" y="858" font-size="12">Crazy - 35 evals</text>
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<text x="116" y="876" font-size="12">Target - 35 evals</text>
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<text x="747" y="100" font-size="15" font-weight="bold">Training losses (log) & alpha (linear)</text>
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<text x="747" y="115" font-size="11" fill="#555">training_metrics.jsonl - big early spikes are normal</text>
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<text x="747" y="133" font-size="12" font-style="italic" fill="#555">What: How well the brain is learning: losses should fall; alpha sets explore/exploit.</text>
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<text x="747" y="149" font-size="12" font-style="italic" fill="#555">Axes: X = training progress; left Y (log) = losses; right Y (linear) = alpha.</text>
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<text x="747" y="165" font-size="12" font-style="italic" fill="#555">Better: lower losses = better; alpha falls over time as the bot gets confident.</text>
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<text x="740" y="788.0" text-anchor="end" font-size="11">0.01</text>
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<text x="740" y="488.0" text-anchor="end" font-size="11">1</text>
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<text x="740" y="338.0" text-anchor="end" font-size="11">10</text>
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<text x="740" y="188.0" text-anchor="end" font-size="11">100</text>
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<text x="1038" y="817" text-anchor="middle" font-size="12">metric line number</text>
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<text x="16" y="484" text-anchor="middle" font-size="12" transform="rotate(-90 16 484)">loss (log)</text>
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<text x="793" y="840" font-size="12">critic_loss</text>
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<text x="793" y="858" font-size="12">|actor_loss|</text>
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<text x="793" y="876" font-size="12">alpha</text>
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<text x="70" y="940" font-size="15" font-weight="bold">Throughput - games per hour</text>
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<text x="70" y="955" font-size="11" fill="#555">method: training_metrics.jsonl 'epoch' deltas; 1 row = one 10-game chunk</text>
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<text x="70" y="973" font-size="12" font-style="italic" fill="#555">What: How many games per hour the bot trains (its learning speed).</text>
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<text x="70" y="989" font-size="12" font-style="italic" fill="#555">Axes: X = training progress (10-game chunks); Y = games per hour.</text>
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<text x="70" y="1005" font-size="12" font-style="italic" fill="#555">Better: higher = faster learning; a steady line beats a spiky one.</text>
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<text x="499.9" y="1511" text-anchor="middle" font-size="11">49</text>
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<text x="63" y="1498.0" text-anchor="end" font-size="11">0</text>
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<text x="63" y="1380.5" text-anchor="end" font-size="11">1653</text>
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<text x="63" y="1263.0" text-anchor="end" font-size="11">3305</text>
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<text x="63" y="1145.5" text-anchor="end" font-size="11">4958</text>
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<text x="383" y="1527" text-anchor="middle" font-size="12">chunk interval (10-game chunks)</text>
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<text x="747" y="940" font-size="15" font-weight="bold">Max score per eval cycle</text>
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<text x="747" y="955" font-size="11" fill="#555">campaign_v4_stdout.log - max of the 10 deterministic round scores per eval</text>
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<text x="747" y="973" font-size="12" font-style="italic" fill="#555">What: Best single-round score the bot managed in each test cycle.</text>
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<text x="747" y="989" font-size="12" font-style="italic" fill="#555">Axes: X = eval cycle number; Y = best score achieved.</text>
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<text x="747" y="1005" font-size="12" font-style="italic" fill="#555">Better: higher is better; a rising trend means the bot is improving.</text>
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<text x="740" y="1263.0" text-anchor="end" font-size="11">103</text>
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<line x1="743" y1="1141.5" x2="747" y2="1141.5" stroke="black"/>
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<text x="740" y="1028.0" text-anchor="end" font-size="11">207</text>
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<text x="1061" y="1527" text-anchor="middle" font-size="12">eval cycle number</text>
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<text x="16" y="1259" text-anchor="middle" font-size="12" transform="rotate(-90 16 1259)">best single-round score</text>
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<text x="793" y="1550" font-size="12">Corners - 35 evals</text>
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<text x="793" y="1568" font-size="12">Crazy - 35 evals</text>
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<text x="793" y="1586" font-size="12">Target - 35 evals</text>
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<text x="793" y="1604" font-size="12">combined max</text>
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<text x="16" y="1666" font-size="15" font-weight="bold">Reading the signs</text>
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<text x="1384" y="1665" text-anchor="end" font-size="11" fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>
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<text x="16" y="1688" font-size="13" font-weight="bold">Healthy patterns ✅</text>
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<text x="16" y="1706" font-size="12">• Both critic and actor losses trending down over time</text>
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<text x="16" y="1720" font-size="12">• Alpha decaying slowly from ~1.0,</text>
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<text x="16" y="1734" font-size="12"> then plateauing — this is normal</text>
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<text x="16" y="1748" font-size="12">• Win rates appearing and increasing in the eval panel</text>
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<text x="16" y="1762" font-size="12">• Max scores rising in the max-score panel</text>
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<text x="16" y="1776" font-size="12">• Alpha plateauing is NOT a problem — it means</text>
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<text x="16" y="1790" font-size="12"> exploration level is stable</text>
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<text x="500" y="1688" font-size="13" font-weight="bold">Warning signs ⚠️</text>
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<text x="500" y="1706" font-size="12">• Losses exploding (suddenly jumping to</text>
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<text x="500" y="1720" font-size="12"> millions or billions)</text>
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<text x="500" y="1734" font-size="12">• Win rates staying at 0% for a long time</text>
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<text x="500" y="1748" font-size="12"> after the first ~20 eval cycles</text>
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<text x="500" y="1762" font-size="12">• Alpha reaching 0 — bot stops exploring entirely</text>
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<text x="500" y="1776" font-size="12"> (gets stuck)</text>
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<text x="500" y="1790" font-size="12">• Max scores flatlining (no improvement</text>
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<text x="500" y="1804" font-size="12"> over many eval cycles)</text>
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<text x="500" y="1818" font-size="12">• Any single loss value above 1e6</text>
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<text x="985" y="1688" font-size="13" font-weight="bold">What each metric means (brief)</text>
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<text x="985" y="1706" font-size="12">• Critic loss: how wrong the bot's value</text>
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<text x="985" y="1720" font-size="12"> estimates are — should go down</text>
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<text x="985" y="1734" font-size="12">• Actor loss: how well the bot's action policy</text>
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<text x="985" y="1748" font-size="12"> is doing — should go down overall</text>
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<text x="985" y="1762" font-size="12"> (some bumps are normal)</text>
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<text x="985" y="1776" font-size="12">• Alpha: exploration-exploitation tradeoff — starts</text>
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<text x="985" y="1790" font-size="12"> high, settles at a positive value (NOT zero)</text>
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<text x="985" y="1804" font-size="12">• Max score: best score achieved per eval cycle —</text>
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<text x="985" y="1818" font-size="12"> rising trend = learning</text>
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