fix(dashboard): harden generation against common failures

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

CONTEXT.md

@@ -0,0 +1,59 @@
+# Evo_Bot
+
+Robocode Tank Royale bot with a modular gun system where evolved neural networks learn to predict enemy dodge behavior.
+
+## Language
+
+**Evo_Bot**:
+The bot itself — handles movement and firing discipline. Guns are pluggable modules.
+_Avoid_: robot, tank
+
+**Guess Factor (GF)**:
+A value from -1 to +1 representing where on the maximum escape angle arc the enemy is. 0 = directly ahead, -1 = full left dodge, +1 = full right dodge. The gun's prediction target.
+_Avoid_: aim offset, dodge index
+
+**Max Escape Angle (MEA)**:
+The widest angle the enemy can reach before a bullet arrives, computed from distance and bullet speed. Guess factor is multiplied by MEA to get the aim offset.
+
+**Lateral Velocity**:
+Enemy speed projected perpendicular to the line between you and them. The primary signal for guess factor prediction.
+_Avoid_: tangential speed, sideways velocity
+
+**Sliding Window**:
+The last N ticks (default 30) of enemy state fed as input to the network. Each tick contains lateral velocity, heading delta, and wall distance ahead.
+_Avoid_: observation buffer, input history
+
+**Replay Tape**:
+Rolling buffer of recorded enemy states (~2000 ticks). The evolution thread evaluates gun fitness against this tape.
+_Avoid_: experience buffer, replay buffer
+
+**Virtual Gun**:
+A gun that runs in parallel without actually firing. It tracks where it would have aimed and whether a simulated bullet would have hit. Used to compare gun variants.
+
+**Virtual Bullet**:
+A simulated bullet fired by a virtual gun. Never actually sent to the game engine.
+
+**TOPO_Gun**:
+Fixed-topology ANN gun evolved by GA. Network shape is predetermined (e.g., 91-8-1), only weights are evolved.
+_Avoid_: static gun, fixed gun
+
+**NEAT_Gun**:
+Variable-topology ANN gun where evolution can add/remove neurons and connections (NEAT algorithm). Deferred — only built if TOPO_Gun hits a ceiling.
+
+**Population**:
+The set of candidate networks (default 64-200) being evolved. Each member is a complete set of ANN weights.
+
+**Champion**:
+The best-performing network in the current GA population. The champion's weights are pushed to the inference side when it beats the current best.
+_Avoid_: best, winner, elite
+
+**Fitness**:
+Hit count when a network's aim predictions are evaluated as virtual bullets against sampled ticks from the replay tape.
+_Avoid_: score, reward
+
+**Cold Start**:
+The first-ever battle with no saved weights. The gun does not fire until the evolution thread produces its first champion. Subsequent battles load persisted weights.
+
+**Weight Persistence**:
+Saving evolved weights to disk. Load order: per-opponent file, then global fallback, then random initialization.
+_Avoid_: model saving, checkpointing

OscillatorBot/OscillatorBot.json

@@ -0,0 +1,11 @@
+{
+  "name": "OscillatorBot",
+  "version": "0.1.0",
+  "authors": ["Davide Cappellini"],
+  "description": "Predictable zigzag sparring partner for gun testing",
+  "homepage": "",
+  "countryCodes": ["IT"],
+  "gameTypes": ["classic", "1v1"],
+  "platform": "Nim",
+  "programmingLang": "Nim"
+}

OscillatorBot/OscillatorBot.nim

@@ -0,0 +1,48 @@
+# OscillatorBot — predictable zigzag sparring partner for GA gun testing.
+# Reverses direction + turn every PERIOD ticks. Head-on targeting only.
+
+import std/[math, os]
+import tankroyale_botapi
+
+const botJsonPath = currentSourcePath().parentDir / "OscillatorBot.json"
+
+const
+  SPEED  = 7.0    # forward/backward speed
+  PERIOD = 25     # ticks between direction reversals
+
+type OscillatorBot = ref object of Bot
+  tickCount: int
+  moveSign:  float  # +1 forward, -1 backward
+  turnSign:  float  # +1 right, -1 left
+
+method onRoundStarted*(bot: OscillatorBot, e: RoundStartedEvent) =
+  setAdjustGunForBodyTurn(true)
+  setAdjustRadarForBodyTurn(true)
+  setAdjustRadarForGunTurn(true)
+  bot.tickCount = 0
+  bot.moveSign  = 1.0
+  bot.turnSign  = 1.0
+
+method onScannedBot*(bot: OscillatorBot, e: ScannedBotEvent) =
+  # Head-on targeting: aim gun directly at enemy, fire medium power
+  let bearing = directionTo(getX(), getY(), e.x, e.y)
+  let gunDelta = normalizeRelativeAngle(bearing - getGunDirection())
+  setGunTurnRate(gunDelta.clamp(-MAX_GUN_TURN_RATE, MAX_GUN_TURN_RATE))
+  if abs(gunDelta) < 10.0 and getGunHeat() <= 0.0:
+    discard setFire(2.0)
+
+method run*(bot: OscillatorBot) =
+  while isRunning():
+    inc bot.tickCount
+    if bot.tickCount mod PERIOD == 0:
+      bot.moveSign *= -1.0
+      bot.turnSign *= -1.0
+
+    setTargetSpeed(bot.moveSign * SPEED)
+    setTurnRate(bot.turnSign * 4.0)
+    setRadarTurnRate(45.0)  # spin radar to keep scanning
+    go()
+
+when isMainModule:
+  var bot = OscillatorBot(moveSign: 1.0, turnSign: 1.0)
+  start(bot, botJsonPath)

OscillatorBot/OscillatorBot.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+cd "$(dirname "$0")"
+exec ./OscillatorBot 2>> /tmp/oscillatorbot_stderr.log

OscillatorBot/config.nims

@@ -0,0 +1 @@
+--path:"../libs"

SAC_LSTM_Bot/tools/plot_progress.py

@@ -3,21 +3,22 @@
 
 Pure-stdlib SVG output (matplotlib not available on this box).
 Generates ONE file:
-   docs/campaign_dashboard.svg - five panels, current (v2) run only:
+   docs/campaign_dashboard.svg - four panels, current (v2) run only:
-     1. test-match win % vs opponents     (campaign_v2_stdout.log eval lines)
+      1. test-match win % vs opponents     (campaign_v4_stdout.log eval lines)
-     2. real-fight win % per opponent     (training_log.jsonl, ~100-game buckets)
+     2. critic_loss / |actor_loss| / alpha (training_metrics.jsonl; losses log
-     3. critic_loss / |actor_loss|        (training_metrics.jsonl, shared log-y)
+                                           left axis, alpha linear right axis)
-     4. alpha temperature                 (training_metrics.jsonl, linear)
+     3. throughput, games/hour buckets    (training_metrics.jsonl 'epoch' deltas;
-     5. throughput, games/hour buckets    (training_metrics.jsonl 'epoch' deltas;
+                                           1 metrics row == one 10-game chunk,
-                                          training_log.jsonl has NO timestamps -
+                                           counts match stdout chunk markers)
-                                          verified field names - and one metrics
+     4. max score per eval cycle          (campaign_v4_stdout.log eval blocks;
-                                          row == one 10-game chunk, counts match
+                                           eval_log.jsonl only ever holds the
-                                          the stdout "=== Chunk N/N ===" markers)
+                                           LATEST cycle, so history comes from
+                                           the stdout log)
    plus an embedded JS snippet that reloads the page every 60 s when the SVG is
    opened as a top-level document in Chrome.
 
 Usage:
-  python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [games_jsonl] [outdir]
+  python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [outdir]
 All args optional; defaults relative to the SAC_LSTM_Bot/ root (parent of tools/).
   python3 tools/plot_progress.py --selftest   # tiny built-in sanity check
 """
@@ -27,48 +28,101 @@ import os
 import re
 import sys
 import tempfile
+import traceback
 from datetime import datetime
 from pathlib import Path
 import xml.etree.ElementTree as ET
 
 ROOT = Path(__file__).resolve().parent.parent
+LOG_FILE = ROOT / "tools" / "plot_progress.log"
+
+
+def log(msg):
+    """Timestamped line to stderr AND tools/plot_progress.log (survives reboots;
+    the watcher has no terminal to read errors from)."""
+    line = f"{datetime.now():%F %T} {msg}"
+    print(line)
+    try:
+        with open(LOG_FILE, "a") as fh:
+            fh.write(line + "\n")
+    except OSError:
+        pass
 EVAL_RE = re.compile(r">>> \[eval\] win rate: (\d+)/(\d+) \(([\d.]+)%\) vs (\S+)")
+EVAL_BLOCK_RE = re.compile(r">>> \[eval\] \d+ deterministic rounds vs (\S+)")
+ROUND_RE = re.compile(r"Round \d+/\d+\D+ticks:\d+ score:(\d+) win:")
 TREND_WINDOW = 10    # rolling mean shown as the thick trend line (panel 1)
-GAME_BUCKET = 100    # games per bucket, real-fight panel
 RATE_BUCKET = 20     # metric intervals per throughput bucket (~200 games)
 GAMES_PER_ROW = 10   # one training_metrics.jsonl row per 10-round chunk
 COLORS = {"Corners": "#d62728", "Crazy": "#1f77b4", "Target": "#2ca02c",
           "RamFire": "#ff7f0e", "SacTwin": "#9467bd"}
 CRITIC_C, ACTOR_C = "#1f77b4", "#ff7f0e"
+ALPHA_C = "#9467bd"  # alpha line on the losses panel
 RELOAD_JS = ('<script type="text/javascript"><![CDATA[ '
              'setTimeout(function(){ location.reload(); }, 60000); ]]></script>')
-W, H = 1400, 2000
+W, H = 1400, 1850
-TITLE_H, GUIDE_H = 80, 180
+HEALTH_H = 205   # bottom "Reading the signs" cheat-sheet band
 # rows: (header_y, panel_top_y, panel_bottom_y, x_left, x_right)
+# panel_top sits low enough to leave room under header+sub for the
+# per-panel how-to-read guide (3 italic lines, see panel_guide)
 C1_L, C1_R = 70, 697
 C2_L, C2_R = 747, 1375
 ROWS = {
-    1: (100, 120, 720, C1_L, C1_R),
+    1: (100, 184, 784, C1_L, C1_R),
-    2: (100, 120, 720, C2_L, C2_R),
+    2: (100, 184, 784, C2_L, C2_R),
-    3: (940, 958, 1428, C1_L, C1_R),
+    3: (940, 1024, 1494, C1_L, C1_R),
-    4: (940, 958, 1428, C2_L, C2_R),
+    4: (940, 1024, 1494, C2_L, C2_R),
-    5: (1600, 1618, 1780, C1_L, C2_R),
 }
 PANEL_TITLES = [
     "Test matches - win % vs opponents",
-    "Real fights - win % per opponent",
+    "Training losses (log) & alpha (linear)",
-    "Training losses (log scale)",
-    "Alpha temperature",
     "Throughput - games per hour",
+    "Max score per eval cycle",
 ]
-GUIDE_LINES = [
+# per-panel how-to-read notes: (what it shows, axes, which direction is better)
-    "Test matches: dots are single fights, thick line shows trend.",
+GUIDES = {
-    "Real battles only. Rising lines mean the bot improves.",
+    1: ("How often the bot wins against each opponent in test battles.",
-    "Loss spikes are normal early; endless growth is bad.",
+        "X = training progress (match number); Y = win rate, 0-100%.",
-    "Alpha high means experimenting; falling too fast freezes habits.",
+        "higher is better."),
-    "Throughput flat is healthy; dips mean something slowed.",
+    2: ("How well the brain is learning: losses should fall; alpha sets explore/exploit.",
-    "This file reloads itself in Chrome every sixty seconds.",
+        "X = training progress; left Y (log) = losses; right Y (linear) = alpha.",
-    "Regenerate anytime with tools/watch_dashboard.sh or the python command.",
+        "lower losses = better; alpha falls over time as the bot gets confident."),
+    3: ("How many games per hour the bot trains (its learning speed).",
+        "X = training progress (10-game chunks); Y = games per hour.",
+        "higher = faster learning; a steady line beats a spiky one."),
+    4: ("Best single-round score the bot managed in each test cycle.",
+        "X = eval cycle number; Y = best score achieved.",
+        "higher is better; a rising trend means the bot is improving."),
+}
+# bottom health-check cheat-sheet: one column per subsection; each bullet is
+# a tuple of pre-wrapped text lines (first line gets the bullet marker)
+SIGNS_COLUMNS = [
+    ("Healthy patterns ✅", [
+        ("Both critic and actor losses trending down over time",),
+        ("Alpha decaying slowly from ~1.0,", "then plateauing — this is normal"),
+        ("Win rates appearing and increasing in the eval panel",),
+        ("Max scores rising in the max-score panel",),
+        ("Alpha plateauing is NOT a problem — it means",
+         "exploration level is stable"),
+    ]),
+    ("Warning signs ⚠️", [
+        ("Losses exploding (suddenly jumping to", "millions or billions)"),
+        ("Win rates staying at 0% for a long time",
+         "after the first ~20 eval cycles"),
+        ("Alpha reaching 0 — bot stops exploring entirely", "(gets stuck)"),
+        ("Max scores flatlining (no improvement",
+         "over many eval cycles)"),
+        ("Any single loss value above 1e6",),
+    ]),
+    ("What each metric means (brief)", [
+        ("Critic loss: how wrong the bot's value",
+         "estimates are — should go down"),
+        ("Actor loss: how well the bot's action policy",
+         "is doing — should go down overall", "(some bumps are normal)"),
+        ("Alpha: exploration-exploitation tradeoff — starts",
+         "high, settles at a positive value (NOT zero)"),
+        ("Max score: best score achieved per eval cycle —",
+         "rising trend = learning"),
+    ]),
 ]
 
 
@@ -92,6 +146,38 @@ def parse_eval_series(path):
     return series
 
 
+def parse_max_scores(path):
+    """Return {opponent: [best single-round score per eval cycle, in file order]}.
+
+    eval_log.jsonl is atomically overwritten every cycle (sac_train.sh mv), so
+    per-cycle history only exists in the stdout log: each eval prints a
+    '>>> [eval] N deterministic rounds vs X' header, then Round/score lines,
+    closed by the '[eval] win rate' (or crashed / no results) line. Training
+    rounds share the Round-line format, so they are ignored unless inside a block.
+    """
+    out, cur, best = {}, None, None
+    if not path.is_file():
+        print(f"[skip] campaign log not found: {path}")
+        return out
+    for line in path.read_text(errors="replace").splitlines():
+        m = EVAL_BLOCK_RE.search(line)
+        if m:
+            cur, best = m.group(1), None
+            continue
+        if cur is None:
+            continue
+        if "[eval]" in line:   # win-rate / crashed / no-results closes the block
+            if best is not None:
+                out.setdefault(cur, []).append(best)
+            cur, best = None, None
+            continue
+        m = ROUND_RE.search(line)
+        if m:
+            v = int(m.group(1))
+            best = v if best is None else max(best, v)
+    return out
+
+
 def parse_metrics(path):
     """Return list of metric dicts, skipping malformed lines."""
     rows = []
@@ -109,25 +195,6 @@ def parse_metrics(path):
     return rows
 
 
-def parse_games(path):
-    """Return [(opponent, won_bool)] for type=='game' rows, skipping junk."""
-    out = []
-    if not path.is_file():
-        print(f"[skip] games log not found: {path}")
-        return out
-    for line in path.read_text(errors="replace").splitlines():
-        try:
-            r = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-        if r.get("type") != "game":
-            continue
-        opp, win = r.get("opponent"), r.get("win")
-        if isinstance(opp, str) and isinstance(win, bool):
-            out.append((opp, win))
-    return out
-
-
 def metric_col(rows, key, positive=False):
     """[(index, value)] for float-parseable rows; abs() applied; optional >0 filter."""
     out = []
@@ -182,8 +249,8 @@ def write_svg(path, text):
     except ET.ParseError as e:
         print(f"[error] {path.name}: generated SVG invalid, keeping old file ({e})")
         return False
-    tmp = path.with_name(path.name + ".tmp")
+    tmp = path.with_name(f"{path.name}.{os.getpid()}.tmp")   # unique: a manual
-    tmp.write_text(text)
+    tmp.write_text(text)                                     # run + watcher can race
     os.replace(tmp, path)
     return True
 
@@ -202,9 +269,9 @@ def dots(pts, color, r=2, opacity=0.25):
                    f'fill="{color}" opacity="{opacity}"/>\n' for x, y in pts)
 
 
-def hgrid(x0, x1, ys):
+def hgrid(x0, x1, ys, color="#dddddd"):
     return "".join(f'<line x1="{x0}" y1="{y:.1f}" x2="{x1}" y2="{y:.1f}" '
-                   f'stroke="#dddddd"/>\n' for y in ys)
+                   f'stroke="{color}"/>\n' for y in ys)
 
 
 def axis(x0, y0, x1, y1, xt, yt, xlabel, ylabel, ylog=False):
@@ -249,21 +316,29 @@ def legend(items, x, y):
 
 def map_fn(p0, p1, vmin, vmax, log=False):
     def f(v):
-        t = (math.log10(v) - vmin) / (vmax - vmin) if log else (v - vmin) / (vmax - vmin)
+        span = (vmax - vmin) or 1   # single-point series -> degenerate range
+        t = (math.log10(v) - vmin) / span if log else (v - vmin) / span
         return p0 + max(0.0, min(1.0, t)) * (p1 - p0)
     return f
 
 
-def guide_block(w, h, lines):
+def signs_block():
-    """Plain-English "How to read" band at the bottom of the canvas."""
+    """Health-check cheat-sheet band along the bottom of the canvas."""
-    y = H - GUIDE_H
+    y0 = H - HEALTH_H
-    s = f'<rect x="0" y="{y}" width="{w}" height="{GUIDE_H}" fill="#f2f2f2"/>\n'
+    s = f'<rect x="0" y="{y0}" width="{W}" height="{HEALTH_H}" fill="#f2f2f2"/>\n'
-    s += (f'<text x="16" y="{y + 21}" font-size="15" font-weight="bold">'
+    s += (f'<text x="16" y="{y0 + 21}" font-size="15" font-weight="bold">'
-          f"How to read</text>\n"
+          "Reading the signs</text>\n"
-          f'<text x="{w - 16}" y="{y + 20}" text-anchor="end" font-size="11" '
+          f'<text x="{W - 16}" y="{y0 + 20}" text-anchor="end" font-size="11" '
-          f'fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>\n')
+          'fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>\n')
-    for i, ln in enumerate(lines):
+    for (head, bullets), x in zip(SIGNS_COLUMNS, (16, 500, 985)):
-        s += f'<text x="16" y="{y + 43 + i * 17}" font-size="13">{esc(ln)}</text>\n'
+        s += (f'<text x="{x}" y="{y0 + 43}" font-size="13" font-weight="bold">'
+              f"{esc(head)}</text>\n")
+        y = y0 + 61
+        for lines in bullets:
+            for j, ln in enumerate(lines):
+                s += (f'<text x="{x}" y="{y}" font-size="12">'
+                      f"{esc(('• ' if j == 0 else '   ') + ln)}</text>\n")
+                y += 14
     return s
 
 
@@ -276,6 +351,16 @@ def header(x, y, title, sub=None):
     return s
 
 
+def panel_guide(x0, y, guide):
+    """Italic how-to-read note between a panel's header and its plot area."""
+    what, axes, better = guide
+    s = ""
+    for i, txt in enumerate((f"What: {what}", f"Axes: {axes}", f"Better: {better}")):
+        s += (f'<text x="{x0}" y="{y + i * 16}" font-size="12" font-style="italic" '
+              f'fill="#555">{esc(txt)}</text>\n')
+    return s
+
+
 # ---------- panels ----------
 
 def panel_test_matches(series, geo):
@@ -284,7 +369,7 @@ def panel_test_matches(series, geo):
     if not series:
         s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
              "no eval lines found</text>\n"
-        return
+        return s
     xmax = max(max(len(v) for v in series.values()), 2)
     xm, ym = map_fn(x0, x1, 1, xmax), map_fn(pb, pt, 0, 100)
     s += hgrid(x0, x1, [ym(v) for v in range(0, 101, 20)])
@@ -305,50 +390,39 @@ def panel_test_matches(series, geo):
     s += legend(items, x0 + 12, pb + 52)
     return s
 
-def panel_real_fights(games, geo):
+def panel_max_score(maxes, geo):
     _, pt, pb, x0, x1 = geo
     s = ""
-    if not games:
+    if not maxes:
         s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
-             "no game rows found</text>\n"
+             "no eval lines found</text>\n"
-        return
+        return s
-    edges = list(range(0, len(games) + 1, GAME_BUCKET))
+    ncyc = max(len(v) for v in maxes.values())
-    if edges[-1] != len(games):
+    xmax = max(ncyc, 2)
-        edges.append(len(games))
+    hi = max(1, max(v for vals in maxes.values() for v in vals)) * 1.1
-    buckets = list(zip(edges[:-1], edges[1:]))
+    xm, ym = map_fn(x0, x1, 1, xmax), map_fn(pb, pt, 0, hi)
-    xm, ym = map_fn(x0, x1, 1, max(len(games), 2)), map_fn(pb, pt, 0, 100)
+    s += hgrid(x0, x1, [ym(hi * k / 4) for k in range(5)])
-    s += hgrid(x0, x1, [ym(v) for v in range(0, 101, 20)])
+    step = max(1, xmax // 8)
-    step = max(GAME_BUCKET, GAME_BUCKET * (len(games) // GAME_BUCKET // 8 + 1))
+    xt = [(str(v), xm(v)) for v in range(step, xmax + 1, step)] or [("1", xm(1))]
-    xt = [(str(v), xm(v)) for v in range(step, len(games) + 1, step)]
+    s += axis(x0, pb, x1, pt, xt, ticks_linear(0, hi, pb, pt, n=5),
-    s += axis(x0, pb, x1, pt, xt, ticks_linear(0, 100, pb, pt, n=6),
+              "eval cycle number", "best single-round score")
-              f"game number ({GAME_BUCKET}-game buckets)", "win %")
+    items, per_opp = [], {}
-    opponents = []
+    for name in ("Corners", "Crazy", "Target"):
-    for opp, _ in games:
+        vals = maxes.get(name, [])
-        if opp not in opponents:
+        if not vals:
-            opponents.append(opp)
+            continue
-    items = []
+        c = COLORS[name]
-    for name in opponents:
+        pts = [(xm(i + 1), ym(v)) for i, v in enumerate(vals)]
-        c = COLORS.get(name, "#7f7f7f")
+        per_opp[name] = vals
-        by_b = {}
+        s += dots(pts, c)
-        for bi, (lo, hi) in enumerate(buckets):
+        s += polyline(pts, c, 2.5)
-            sub = [w for o, w in games[lo:hi] if o == name]
+        items.append((c, f"{name} - {len(vals)} evals"))
-            if sub:
+    if len(items) >= 2:   # combined best across opponents, cycle-aligned
-                by_b[bi] = 100.0 * sum(sub) / len(sub)
+        comb = [max(vals[i] for vals in per_opp.values() if i < len(vals))
-        pts = []
+                for i in range(ncyc)]
-        segs, prev = [], None
+        s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(comb)],
-        for bi in sorted(by_b):
+                      "#555555", 2.5, dash="6 4")
-            if prev is not None and bi != prev + 1:
+        items.append(("#555555", "combined max"))
-                segs.append(pts)
-                pts = []
-            center = (buckets[bi][0] + buckets[bi][1]) / 2
-            pts.append((xm(center), ym(by_b[bi])))
-            prev = bi
-        if len(pts) >= 2:
-            segs.append(pts)
-        for seg in segs:
-            s += polyline(seg, c, 3.5)
-        n_played = sum(1 for o, _ in games if o == name)
-        items.append((c, f"{name} ({n_played} games)"))
     s += legend(items, x0 + 12, pb + 52)
     return s
 
@@ -358,44 +432,50 @@ def panel_losses(rows, geo):
     critic = metric_col(rows, "critic_loss", positive=True)
     actor = metric_col(rows, "actor_loss")  # abs() applied; sign dropped
     actor = [(i, v) for i, v in actor if v > 0]
-    if not (critic or actor):
+    alpha = metric_col(rows, "alpha")
+    if not (critic or actor or alpha):
         s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
              "no valid loss points</text>\n"
-        return
+        return s
-    allv = [v for _, v in critic + actor]
+    x1 -= 46   # room on the right for the twin alpha axis labels
-    lo, hi = math.floor(math.log10(min(allv))), math.ceil(math.log10(max(allv)))
-    if lo == hi:
-        hi = lo + 1
     n = len(rows)
     xm = lambda i: x0 + (x1 - x0) * i / max(n - 1, 1)
+    # log-domain source (losses in practice); drop non-positive values so a
+    # run of alpha==0 rows can't raise math domain error and kill the build
+    base = [(i, v) for i, v in critic + actor if v > 0] or \
+           [(i, v) for i, v in alpha if v > 0]
+    if not base:
+        s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
+             "no valid loss points</text>\n"
+        return s
+    lo = math.floor(math.log10(min(v for _, v in base)))
+    hi = math.ceil(math.log10(max(v for _, v in base)))
+    if lo == hi:
+        hi = lo + 1
     ym = map_fn(pb, pt, lo, hi, log=True)
     s += hgrid(x0, x1, [ym(10 ** e) for e in range(lo, hi + 1)])
     s += axis(x0, pb, x1, pt, ticks_linear(1, n, x0, x1, n=5),
               ticks_log(lo, hi, pb, pt), "metric line number", "loss (log)")
     s += polyline([(xm(i), ym(v)) for i, v in critic], CRITIC_C, 1.8)
     s += polyline([(xm(i), ym(v)) for i, v in actor], ACTOR_C, 1.8)
-    s += legend([(CRITIC_C, "critic_loss"), (ACTOR_C, "|actor_loss|")],
+    if alpha:   # twin axis: alpha on its own linear scale, purple like the line
-                x0 + 12, pb + 52)
+        ahi = max(1.0, max(v for _, v in alpha))
-    return s
+        yma = map_fn(pb, pt, 0, ahi)
-
+        s += hgrid(x0, x1, [yma(ahi * k / 4) for k in range(5)], "#e9dcf5")
-def panel_alpha(rows, geo):
+        mid = (pt + pb) // 2
-    _, pt, pb, x0, x1 = geo
+        s += f'<line x1="{x1}" y1="{pb}" x2="{x1}" y2="{pt}" stroke="{ALPHA_C}"/>\n'
-    s = ""
+        for v, py in ticks_linear(0, ahi, pb, pt, n=5, fmt="{:.3g}"):
-    alpha = metric_col(rows, "alpha")
+            s += (f'<line x1="{x1}" y1="{py:.1f}" x2="{x1 + 4}" y2="{py:.1f}" '
-    if not alpha:
+                  f'stroke="{ALPHA_C}"/>\n'
-        s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
+                  f'<text x="{x1 + 7}" y="{py + 4:.1f}" font-size="11" '
-             "no alpha points</text>\n"
+                  f'fill="{ALPHA_C}">{esc(v)}</text>\n')
-        return
+        s += (f'<text x="{x1 + 17}" y="{mid}" text-anchor="middle" font-size="12" '
-    n = len(rows)
+              f'fill="{ALPHA_C}" transform="rotate(90 {x1 + 17} {mid})">alpha</text>\n')
-    hi = max(1.0, max(v for _, v in alpha))
+        s += polyline([(xm(i), yma(v)) for i, v in alpha], ALPHA_C, 1.8)
-    xm = lambda i: x0 + (x1 - x0) * i / max(n - 1, 1)
+    items = [(CRITIC_C, "critic_loss"), (ACTOR_C, "|actor_loss|")]
-    ym = map_fn(pb, pt, 0, hi)
+    if alpha:
-    s += hgrid(x0, x1, [ym(v) for v in
+        items.append((ALPHA_C, "alpha"))
-                        [hi * k / 4 for k in range(5)]])
+    s += legend(items, x0 + 12, pb + 52)
-    s += axis(x0, pb, x1, pt, ticks_linear(1, n, x0, x1, n=5),
-              ticks_linear(0, hi, pb, pt, n=5, fmt="{:.3g}"),
-              "metric line number", "alpha")
-    s += polyline([(xm(i), ym(v)) for i, v in alpha], "#9467bd", 1.8)
     return s
 
 def panel_throughput(rows, geo):
@@ -415,7 +495,7 @@ def panel_throughput(rows, geo):
     if not rates:
         s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
              "no usable epoch timestamps</text>\n"
-        return
+        return s
     bm = bucket_means(rates, RATE_BUCKET)
     xm = map_fn(x0, x1, 1, len(rates))
     ymax = max(max(rates), max(bm)) * 1.1
@@ -434,14 +514,15 @@ def panel_throughput(rows, geo):
 
 # ---------- assembly ----------
 
-def build_dashboard(campaign, metrics_f, games_f, out):
+def build_dashboard(campaign, metrics_f, out):
     series = parse_eval_series(campaign)
     print("[info] evals parsed: " +
           (", ".join(f"{k}={len(v)}" for k, v in sorted(series.items())) or "(none)"))
+    maxes = parse_max_scores(campaign)
+    print("[info] eval max-score cycles parsed: " +
+          (", ".join(f"{k}={len(v)}" for k, v in sorted(maxes.items())) or "(none)"))
     rows = parse_metrics(metrics_f)
     print(f"[info] metric rows parsed: {len(rows)}")
-    games = parse_games(games_f)
-    print(f"[info] game rows parsed: {len(games)}")
 
     s = (f'<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" '
          f'viewBox="0 0 {W} {H}" font-family="sans-serif">\n'
@@ -453,28 +534,31 @@ def build_dashboard(campaign, metrics_f, games_f, out):
          f'(open this file in Chrome)</text>\n')
 
     drawers = [
-        (ROWS[1], PANEL_TITLES[0],
+        (ROWS[1], PANEL_TITLES[0], GUIDES[1],
          "raw dots = single test matches, thick = rolling-mean-%d" % TREND_WINDOW,
          lambda: panel_test_matches(series, ROWS[1])),
-        (ROWS[2], PANEL_TITLES[1],
+        (ROWS[2], PANEL_TITLES[1], GUIDES[2],
-         "training_log.jsonl only - learning in REAL battles, not tests",
-         lambda: panel_real_fights(games, ROWS[2])),
-        (ROWS[3], PANEL_TITLES[2],
          "training_metrics.jsonl - big early spikes are normal",
-         lambda: panel_losses(rows, ROWS[3])),
+         lambda: panel_losses(rows, ROWS[2])),
-        (ROWS[4], PANEL_TITLES[3],
+        (ROWS[3], PANEL_TITLES[2], GUIDES[3],
-         "training_metrics.jsonl - high = exploring, low = exploiting",
+         "method: training_metrics.jsonl 'epoch' deltas; 1 row = one 10-game chunk",
-         lambda: panel_alpha(rows, ROWS[4])),
+         lambda: panel_throughput(rows, ROWS[3])),
-        (ROWS[5], PANEL_TITLES[4],
+        (ROWS[4], PANEL_TITLES[3], GUIDES[4],
-         "method: training_metrics.jsonl 'epoch' deltas (training_log.jsonl has "
+         "campaign_v4_stdout.log - max of the 10 deterministic round scores per eval",
-         "no timestamps); 1 row = one 10-game chunk",
+         lambda: panel_max_score(maxes, ROWS[4])),
-         lambda: panel_throughput(rows, ROWS[5])),
     ]
-    for geo, title, sub, drawer in drawers:
+    for geo, title, guide, sub, drawer in drawers:
         s += header(geo[3], geo[0], title, sub)
-        s += drawer()
+        s += panel_guide(geo[3], geo[0] + 33, guide)
+        try:
+            body = drawer()
+        except Exception as e:   # one bad panel must not kill the whole page
+            log(f"[warn] panel '{title}' failed, rendering placeholder: {e}")
+            body = (f'<text x="{geo[3] + 10}" y="{geo[1] + 40}" font-size="12" '
+                    f'fill="#a00">panel error: {esc(e)}</text>')
+        s += body or ""   # panels bare-return None on their no-data path
 
-    s += guide_block(W, H, GUIDE_LINES)
+    s += signs_block()
     s += RELOAD_JS + "\n"
     s += "</svg>\n"
     return write_svg(out, s)
@@ -486,14 +570,28 @@ def selftest():
     with tempfile.TemporaryDirectory() as td:
         td = Path(td)
         (td / "log").write_text(
-            ">>> [eval] win rate: 3/10 (30%) vs Corners\n"
+            ">>> [eval] 10 deterministic rounds vs Corners\n"
+            "Round 1/10 - ticks:100 score:3 win:true\n"
             "garbage line\n"
+            "Round 2/10 - ticks:100 score:7 win:false\n"
+            ">>> [eval] win rate: 3/10 (30%) vs Corners\n"
+            ">>> [eval] 10 deterministic rounds vs Crazy\n"
+            "Round 1/10 - ticks:100 score:70 win:false\n"
             ">>> [eval] win rate: 7/10 (70%) vs Crazy\n"
-            ">>> [eval] win rate: broken\n"
+            ">>> [eval] broken\n"
+            "Round 9/9 - ticks:1 score:999 win:false\n"
+            ">>> [eval] 10 deterministic rounds vs Corners\n"
+            "Round 1/10 - ticks:100 score:50 win:true\n"
             ">>> [eval] win rate: 5/10 (50%) vs Corners\n"
+            ">>> [eval] 10 deterministic rounds vs Crazy\n"
+            "Round 1/10 - ticks:100 score:40 win:true\n"
             ">>> [eval] win rate: 4/10 (40%) vs Crazy\n")
         ser = parse_eval_series(td / "log")
         assert ser == {"Corners": [30.0, 50.0], "Crazy": [70.0, 40.0]}, ser
+        mxs = parse_max_scores(td / "log")
+        # stray Round 999 after the unclosed '[eval] broken' line is ignored;
+        # per-cycle max of the Round scores above
+        assert mxs == {"Corners": [7, 50], "Crazy": [70, 40]}, mxs
         assert rolling([10] * 25, 20)[-1] == 10.0
         assert rolling([1, 2, 3], 20) == [1.0, 1.5, 2.0]
         assert len(bucket_means(list(range(1287)), RATE_BUCKET)) == RATE_BUCKET
@@ -506,32 +604,39 @@ def selftest():
             '{"epoch": 1090.0, "critic_loss": 50, "actor_loss": 3, "alpha": 0.2}\n')
         rows = parse_metrics(td / "m.jsonl")
         assert len(rows) == 3 and rows[1]["critic_loss"] == 100
-        glines = []
-        for i in range(150):  # 2 full GAME_BUCKETs, both opponents in both
-            glines.append(json.dumps(
-                {"type": "game", "round": i % 10 + 1, "ticks": 100,
-                 "score": i % 3, "total_score": i, "win": i % 3 == 0,
-                 "opponent": ("Corners", "Crazy")[i % 2]}))
-        (td / "g.jsonl").write_text("\n".join(glines) + "\n")
-        games = parse_games(td / "g.jsonl")
-        assert len(games) == 150 and games[0] == ("Corners", True)
-        assert games[-1] == ("Crazy", False)  # i=149: odd -> Crazy; 149%3!=0 -> loss
         assert metric_col(rows, "actor_loss") == [(0, 2.0), (1, 4.0), (2, 3.0)]
 
         dash = td / "dash.svg"
-        assert build_dashboard(td / "log", td / "m.jsonl", td / "g.jsonl", dash)
+        assert build_dashboard(td / "log", td / "m.jsonl", dash)
         text = dash.read_text()
         ET.fromstring(text)  # whole doc must parse -> closing tag present
         assert RELOAD_JS in text, "auto-reload script missing"
         for t in PANEL_TITLES:
-            assert t in text, f"panel title missing: {t}"
+            assert esc(t) in text, f"panel title missing: {t}"
         assert text.count(PANEL_TITLES[0]) == 1
-        assert "How to read" in text, "reading guide missing"
+        assert "Reading the signs" in text, "health-check section missing"
-        assert 'width="1400"' in text and 'height="2000"' in text
+        for head, bullets in SIGNS_COLUMNS:
-        # circles: 4 eval dots (panel 1) + 2 throughput rate dots (panel 5)
+            assert esc(head) in text, f"health-check column missing: {head}"
-        assert text.count("<circle") == 6, text.count("<circle")
+            for lines in bullets:
-        # 2 trends + 2 real-fight opp lines + 2 losses + 1 alpha + 1 throughput
+                assert esc("• " + lines[0]) in text, f"bullet missing: {lines[0]}"
-        assert text.count("<polyline") == 8, text.count("<polyline")
+        assert text.count("• ") == sum(len(b) for _, b in SIGNS_COLUMNS)
+        assert 'width="1400"' in text and 'height="1850"' in text
+        # every panel carries its own What/Axes/Better how-to-read note
+        assert text.count("What:") == len(PANEL_TITLES), text.count("What:")
+        for g in GUIDES.values():
+            assert esc(g[0]) in text, f"panel guide missing: {g[0]}"
+        # circles: 4 eval dots (panel 1) + 2 throughput rate dots (panel 3)
+        #          + 2 cycles x 2 opponents max-score dots (panel 4)
+        assert text.count("<circle") == 10, text.count("<circle")
+        # 2 trends + 3 losses-panel polylines (critic, actor, alpha)
+        # + 1 throughput
+        # + 3 max-score panel (Corners, Crazy, combined; Target absent in fixture)
+        assert text.count("<polyline") == 9, text.count("<polyline")
+        # losses panel twin axis: "alpha" text = legend + right ylabel; purple
+        # fills = 5 right-axis tick labels + rotated ylabel
+        assert text.count(">alpha<") == 2, text.count(">alpha<")
+        assert text.count('fill="#9467bd"') == 6, text.count('fill="#9467bd"')
+        assert "combined max" in text, "max-score combined line missing"
 
         # orientation guard: a known rising series (10% -> 90%) rendered through
         # the FULL build path must plot upward (smaller SVG y) and forward in
@@ -541,13 +646,39 @@ def selftest():
             ">>> [eval] win rate: 1/10 (10%) vs Corners\n"
             ">>> [eval] win rate: 9/10 (90%) vs Corners\n")
         ori_dash = td / "ori.svg"
-        assert build_dashboard(ori_log, td / "m.jsonl", td / "g.jsonl", ori_dash)
+        assert build_dashboard(ori_log, td / "m.jsonl", ori_dash)
         m = re.search(r'<polyline points="([^"]+)"', ori_dash.read_text())
         pts = [tuple(map(float, p.split(","))) for p in m.group(1).split()]
         assert len(pts) == 2, pts
         (xa, ya), (xb, yb) = pts
         assert yb < ya, f"y-axis inverted: win % rose 10->90 but ink moved down ({ya} -> {yb})"
         assert xb > xa, f"x-axis reversed: newer eval plotted left ({xa} -> {xb})"
+
+        # resilience: missing/empty inputs render placeholders, never crash
+        empty_dash = td / "empty.svg"
+        assert build_dashboard(td / "nope.log", td / "nope.jsonl", empty_dash)
+        etext = empty_dash.read_text()
+        assert etext.count("no eval lines found") == 2, etext.count("no eval lines found")
+        assert "no valid loss points" in etext
+        assert "no usable epoch timestamps" in etext
+
+        # all-zero alpha rows (post-crash trainer state) must not raise in the
+        # losses panel's log-domain math -> placeholder instead of dead build
+        (td / "zero.jsonl").write_text(
+            '{"epoch": 1000.0, "alpha": 0}\n{"epoch": 1060.0, "alpha": 0}\n')
+        zero_dash = td / "zero.svg"
+        assert build_dashboard(td / "log", td / "zero.jsonl", zero_dash)
+        assert "no valid loss points" in zero_dash.read_text()
+
+        # single throughput interval (fresh 2-row metrics file right after a
+        # restart) used to divide by zero in map_fn and kill the whole build
+        (td / "one.jsonl").write_text(
+            '{"epoch": 1000.0, "critic_loss": 10, "alpha": 0.5}\n'
+            '{"epoch": 1060.0, "critic_loss": 5, "alpha": 0.4}\n')
+        one_dash = td / "one.svg"
+        assert build_dashboard(td / "log", td / "one.jsonl", one_dash)
+        assert "panel error" not in one_dash.read_text()
+        assert "<polyline" in one_dash.read_text()
     print("selftest OK")
 
 
@@ -556,21 +687,24 @@ def main():
         selftest()
         return
     args = [a for a in sys.argv[1:] if not a.startswith("-")]
-    campaign = Path(args[0]) if len(args) > 0 else ROOT / "campaign_v2_stdout.log"
+    campaign = Path(args[0]) if len(args) > 0 else ROOT / "campaign_v4_stdout.log"
     metrics = Path(args[1]) if len(args) > 1 else ROOT / "training_metrics.jsonl"
-    games = Path(args[2]) if len(args) > 2 else ROOT / "training_log.jsonl"
+    outdir = Path(args[2]) if len(args) > 2 else ROOT / "docs"
-    outdir = Path(args[3]) if len(args) > 3 else ROOT / "docs"
-    outdir.mkdir(parents=True, exist_ok=True)
 
     try:
-        ok = build_dashboard(campaign, metrics, games, outdir / "campaign_dashboard.svg")
+        outdir.mkdir(parents=True, exist_ok=True)
-    except Exception as e:
+        for p in (campaign, metrics):
-        print(f"[error] dashboard build failed: {e}")
+            if not p.is_file():
+                # loudest symptom of a watcher launched from a stale checkout
+                log(f"[warn] input missing: {p} - is this the live checkout?")
+        ok = build_dashboard(campaign, metrics, outdir / "campaign_dashboard.svg")
+    except Exception:
+        log(f"[error] dashboard build failed:\n{traceback.format_exc().rstrip()}")
         ok = False
     if ok:
         print(f"[done] dashboard written to {outdir / 'campaign_dashboard.svg'}")
     else:
-        print("[error] dashboard NOT updated - check paths above")
+        print("[error] dashboard NOT updated - see " + str(LOG_FILE))
         sys.exit(1)
 
 

SAC_LSTM_Bot/tools/watch_dashboard.sh

@@ -1,10 +1,23 @@
 #!/bin/sh
 # Keep docs/campaign_dashboard.svg fresh: regenerate every 60 s.
-# Errors go to stderr and never exit the loop silently.
+# Failures are appended to tools/plot_progress.log (by this wrapper and by
-dir=$(dirname "$0")
+# plot_progress.py itself) - check that file when the dashboard looks stale.
+# The startup banner records WHICH tree this instance watches, so a copy
+# launched from a stale checkout (the post-reboot failure mode) is visible.
+set -u
+dir=$(cd "$(dirname "$0")" && pwd)
+log="$dir/plot_progress.log"
+
+if command -v flock >/dev/null 2>&1; then
+    exec 9>"$dir/.watch_dashboard.lock"
+    flock -n 9 || { echo "[watch_dashboard] another instance already running, exiting" >&2; exit 0; }
+fi
+
+echo "[watch_dashboard] $(date '+%F %T') started, watching root=$(cd "$dir/.." && pwd)" >> "$log" 2>/dev/null || true
 while :; do
     if ! python3 "$dir/plot_progress.py"; then
-        echo "[watch_dashboard] $(date '+%F %T') regeneration failed (see error above)" >&2
+        echo "[watch_dashboard] $(date '+%F %T') regeneration failed (see $log)" >&2
+        echo "[watch_dashboard] $(date '+%F %T') regeneration failed" >> "$log" 2>/dev/null || true
     fi
     sleep 60
 done

docs/adr/0001-neuroevolution-gun-fixed-topology-ann-evolved-by-ga.md

@@ -0,0 +1,46 @@
+# Neuroevolution gun with fixed-topology ANN evolved by GA
+
+Evo_Bot needs a gun that adapts to each opponent's dodge patterns during a match, finds nonlinear movement patterns that histograms miss, and is original. We chose a fixed-topology feedforward ANN (91->8->1, 745 weights) whose weights are evolved by a mutation-only GA running on a parallel thread. This beats the alternatives (RL too slow to adapt in-match, Q-learning collapses to histogram for single-shot decisions, guess-factor histograms are unoriginal, transformer/LLM-style prediction is data-starved at ~14k ticks per match) while keeping implementation risk low by deferring topology evolution (NEAT) until the fixed network hits its ceiling.
+
+## Considered Options
+
+- **PPO / SAC (end-to-end RL):** Too slow -- thousands of rounds to converge, cannot adapt mid-match. Explored in other bots in this repo.
+- **Q-learning for aiming:** Collapses to a histogram. Single-shot aiming has no sequential decision structure for Q-learning to exploit.
+- **Guess-factor histogram:** Proven and fast to converge (~15 ticks), but unoriginal -- 20 years of community tuning.
+- **Transformer / LLM-style sequence prediction:** Data-starved. ~14k ticks per match vs billions needed for attention-based models.
+- **GA with crossover:** Literature uniformly shows crossover is harmful for ANN weight evolution -- it breaks co-adapted weight configurations. Every modern neuroevolution paper (Uber Deep GA, NRA, OpenAI ES) drops it.
+- **CMA-ES:** Ideal at d=750 weights (self-adapts sigma and covariance). More complex to implement; upgrade path from simple GA when needed.
+
+## Decision
+
+**Architecture:**
+- Evo_Bot (1v1) with modular gun interface: `feed(state)` / `aim() -> (angle, power)`
+- Gun owns its evolution thread (parallel, never blocks inference)
+
+**TOPO_Gun (first gun implementation):**
+- Network: 91->8->1 (hidden size configurable), 745 weights
+- Input: 30 ticks x (lateral_vel, delta_heading, wall_distance_ahead) + current distance = 91
+- Output: guess factor (-1 to +1)
+- Bullet power: deterministic distance-based formula (not learned)
+- Evolution: population 300, clone loaded champion + small Gaussian mutations (ALL weights, sigma=0.005-0.01), NO crossover, single elite preserved unchanged
+- Fitness: virtual bullet hits on 100 randomly sampled replay tape ticks, using real distance-based power
+- Replay tape: rolling window ~2000 ticks (configurable)
+- Weight persistence: per-opponent -> global fallback -> random init (load order)
+- Cold start: first-ever run, don't fire until champion emerges; subsequent runs load weights, fire from tick 1
+- Push new champion weights to inference when it hits better than current
+
+**Deferred:**
+- NEAT_Gun: deferred until TOPO_Gun hits its ceiling
+- Virtual Guns: run multiple guns in parallel, fire whichever has best virtual hit rate
+
+**Boundaries:**
+- Bot controls firing discipline (when to shoot, energy management); gun always returns best aim
+
+## Consequences
+
+- GA+ANN finds nonlinear patterns histograms miss, but needs more data (~50+ ticks vs ~15 for guess-factor histogram) before it outperforms
+- Fixed topology before NEAT reduces implementation risk
+- Mutation-only evolution simplifies implementation (no crossover logic)
+- Parallel evolution thread reuses the pattern from SAC_LSTM_Bot (#48)
+- Per-opponent weight persistence eliminates cold start after first encounter
+- CMA-ES is the natural upgrade path if simple GA convergence is too slow (d=750 is CMA-ES sweet spot)

prototypes/ga_gun_spike/ga_spike.nim

@@ -0,0 +1,111 @@
+## ga_spike.nim — throwaway GA neuroevolution spike: evolve ANN to predict sin(x)
+## Validates: population init, fitness eval, truncation selection, gaussian
+## mutation on all weights, single-elite preservation, champion tracking.
+## Issue #66.
+
+import std/[math, random, algorithm, strformat]
+
+const
+  InputDim  = 10   # sliding window of 10 past sin values
+  HiddenDim = 4
+  OutputDim = 1
+  NumWeights = (InputDim * HiddenDim + HiddenDim) +  # W1 + b1
+               (HiddenDim * OutputDim + OutputDim)    # W2 + b2  = 49
+  PopSize    = 200
+  Generations = 200
+  TopFrac    = 0.2  # keep top 20%
+  Sigma      = 0.01 # gaussian mutation sigma on ALL weights
+  EvalPoints = 50   # fitness eval sample size
+
+type Individual = object
+  weights: seq[float64]
+  fitness: float64
+
+proc forward(w: seq[float64]; input: array[InputDim, float64]): float64 =
+  ## 10->4->1 tanh ANN, flat weight layout: W1[40], b1[4], W2[4], b2[1]
+  var hidden: array[HiddenDim, float64]
+  for h in 0..<HiddenDim:
+    var s = w[InputDim * HiddenDim + h]  # b1[h]
+    for i in 0..<InputDim:
+      s += w[h * InputDim + i] * input[i]  # W1[h,i]
+    hidden[h] = tanh(s)
+  var s = w[InputDim * HiddenDim + HiddenDim + HiddenDim]  # b2[0]
+  for h in 0..<HiddenDim:
+    s += w[InputDim * HiddenDim + HiddenDim + h] * hidden[h]  # W2[h]
+  result = tanh(s)
+
+proc evaluate(ind: var Individual; rng: var Rand) =
+  ## Fitness = -MSE on EvalPoints random samples of sin prediction.
+  var mse = 0.0
+  for _ in 0..<EvalPoints:
+    let x = rng.rand(0.0 .. 20.0 * PI)
+    var input: array[InputDim, float64]
+    for i in 0..<InputDim:
+      input[i] = sin(x - float64(InputDim - 1 - i))
+    let target = sin(x)
+    let pred = forward(ind.weights, input)
+    mse += (pred - target) * (pred - target)
+  ind.fitness = -mse / EvalPoints.float64
+
+proc mutate(ind: var Individual; rng: var Rand) =
+  ## Additive gaussian on ALL weights, per GA-parameters research.
+  for i in 0..<ind.weights.len:
+    ind.weights[i] += rng.gauss(0.0, Sigma)
+
+when isMainModule:
+  var rng = initRand(42)
+
+  # Init population: random weights in [-0.5, 0.5]
+  var pop = newSeq[Individual](PopSize)
+  for i in 0..<PopSize:
+    pop[i].weights = newSeq[float64](NumWeights)
+    for j in 0..<NumWeights:
+      pop[i].weights[j] = rng.rand(-0.5 .. 0.5)
+
+  echo &"GA spike: {PopSize} individuals, {NumWeights} weights, {Generations} gens, sigma={Sigma}"
+
+  for gen in 0..<Generations:
+    # Evaluate
+    for i in 0..<PopSize:
+      pop[i].evaluate(rng)
+
+    # Sort descending by fitness (higher = better)
+    pop.sort(proc(a, b: Individual): int = cmp(b.fitness, a.fitness))
+
+    if gen mod 20 == 0 or gen == Generations - 1:
+      echo &"  gen {gen:3d}  best_fitness={pop[0].fitness:.6f}  (MSE={-pop[0].fitness:.6f})"
+
+    # Selection: keep top 20%
+    let nParents = max(1, int(PopSize.float64 * TopFrac))
+
+    # Next generation: elite(1) + mutated children from parents
+    var next = newSeq[Individual](PopSize)
+    next[0] = pop[0]  # single elite, unchanged
+    for i in 1..<PopSize:
+      let parent = rng.rand(0..<nParents)
+      next[i].weights = pop[parent].weights  # clone parent
+      next[i].mutate(rng)
+    pop = next
+
+  # Final evaluation of champion
+  pop[0].evaluate(rng)
+  echo &"\nChampion fitness: {pop[0].fitness:.6f}  (MSE={-pop[0].fitness:.6f})"
+
+  # Print predictions vs actual
+  echo "\nPredictions (sample):"
+  var totalErr = 0.0
+  let nSamples = 20
+  for i in 0..<nSamples:
+    let x = float64(i) * 1.0
+    var input: array[InputDim, float64]
+    for j in 0..<InputDim:
+      input[j] = sin(x - float64(InputDim - 1 - j))
+    let target = sin(x)
+    let pred = forward(pop[0].weights, input)
+    totalErr += abs(pred - target)
+    echo &"  x={x:5.1f}  sin(x)={target:+.4f}  pred={pred:+.4f}  err={abs(pred-target):.4f}"
+
+  let avgErr = totalErr / nSamples.float64
+  echo &"\nAverage absolute error: {avgErr:.4f}"
+  assert avgErr < 0.1, &"Champion avg error {avgErr:.4f} >= 0.1 — evolution did not converge"
+  echo "PASS: assert avgErr < 0.1"