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feat(ModularBot): 6 guns, pattern matcher, melee modules, adversarial bots

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- New guns: guess-factor (GF histogram), pattern-matcher (movement tape replay)
- New modules: minimum-risk melee movement, spinning melee radar
- New test bots: PatternMover, RandomMover, WaveSurfer
- Fixed: FeedbackEvent now carries actualX/actualY for proper GF learning
- Fixed: TM gun warmup gating + directional residuals
- Fixed: circular gun integrated formula + multi-bin omega cache
- Fixed: oscillator wall-bounce lockout
- Fixed: phantom meteor perpendicular body orientation
- 6/6 battle wins across all enemy types
This commit is contained in:
Davide Cappellini
2026-09-20 00:59:53 +02:00
parent 254c7dc997
commit 1ed7797cb6
184 changed files with 80149 additions and 21 deletions
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BNNBot_garage/analysis/backtest_report.txt
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======================================================================
BACKTEST REPORT
Rows: 277, Distance band splits: 19.0 / 27.0
======================================================================
--- PREDICTOR 1: Pure Linear Extrapolation ---
Power MAE RMSE Median Max %<5u
--------------------------------------------------
p0.10 8.10 11.93 5.00 61.09 49.8%
p0.42 8.68 12.70 5.33 64.35 49.1%
p0.74 9.37 13.59 5.75 67.94 46.6%
p1.07 10.18 14.62 6.24 72.04 40.1%
p1.39 11.03 15.72 7.26 76.48 38.6%
p1.71 11.97 16.96 8.38 81.92 35.7%
p2.03 13.22 18.49 9.62 87.57 32.5%
p2.36 14.64 20.24 11.00 94.46 29.2%
p2.68 16.46 22.43 12.77 102.17 26.4%
p3.00 18.70 25.16 14.00 111.15 23.5%
--- PREDICTOR 2: Weighted Multi-Frame Velocity ---
Power MAE RMSE Median Max %<5u
--------------------------------------------------
p0.10 7.46 10.80 4.62 46.26 52.0%
p0.42 8.04 11.53 4.96 48.78 51.3%
p0.74 8.68 12.34 5.53 51.55 47.3%
p1.07 9.45 13.30 6.18 54.69 42.2%
p1.39 10.30 14.35 6.78 58.09 39.4%
p1.71 11.21 15.50 8.07 62.47 35.7%
p2.03 12.37 16.94 9.02 66.76 32.9%
p2.36 13.75 18.59 10.47 72.08 27.4%
p2.68 15.48 20.66 11.94 78.02 24.5%
p3.00 17.64 23.29 13.82 84.88 21.3%
--- PREDICTOR 3: Linear + Hebbian Residual Correction ---
Learning rate = 0.05
Power MAE RMSE Median Max %<5u
--------------------------------------------------
p0.10 6.92 10.09 4.47 46.22 53.1%
p0.42 7.46 10.79 4.88 49.45 52.0%
p0.74 8.12 11.63 5.33 53.03 48.0%
p1.07 8.91 12.62 5.92 57.12 44.4%
p1.39 9.74 13.70 6.69 61.56 39.7%
p1.71 10.76 14.92 7.84 66.93 36.8%
p2.03 11.99 16.44 9.40 72.59 33.2%
p2.36 13.44 18.21 10.56 79.47 30.3%
p2.68 15.29 20.45 12.49 87.18 27.8%
p3.00 17.55 23.26 14.04 96.16 24.9%
Learning curve (p1.07): first-50 MAE=18.44 last-50 MAE=5.27
Learning rate = 0.1
Power MAE RMSE Median Max %<5u
--------------------------------------------------
p0.10 6.43 9.49 4.24 41.84 54.9%
p0.42 6.92 10.09 4.61 44.94 52.3%
p0.74 7.57 10.85 5.14 48.39 49.5%
p1.07 8.34 11.76 5.61 52.47 45.8%
p1.39 9.15 12.77 6.30 56.79 39.7%
p1.71 10.14 13.95 7.43 61.80 35.0%
p2.03 11.37 15.43 8.83 67.33 32.1%
p2.36 12.85 17.18 10.35 74.19 29.6%
p2.68 14.72 19.42 12.19 81.71 27.8%
p3.00 16.98 22.23 14.24 90.49 26.7%
Learning curve (p1.07): first-50 MAE=17.13 last-50 MAE=3.87
Learning rate = 0.2
Power MAE RMSE Median Max %<5u
--------------------------------------------------
p0.10 6.00 9.11 3.84 39.56 58.5%
p0.42 6.28 9.53 3.78 39.58 58.1%
p0.74 6.77 10.15 4.32 42.34 56.3%
p1.07 7.48 10.92 5.15 46.36 48.0%
p1.39 8.30 11.82 5.94 50.66 46.2%
p1.71 9.29 12.91 6.63 55.64 37.9%
p2.03 10.52 14.32 7.98 61.16 31.8%
p2.36 11.97 16.02 9.51 67.99 28.9%
p2.68 13.85 18.23 11.44 75.50 25.3%
p3.00 16.15 21.04 13.27 84.26 23.8%
Learning curve (p1.07): first-50 MAE=14.85 last-50 MAE=2.62
--- PREDICTOR 3: Residual Table after training (lr=0.1) ---
Sector Band corr_x corr_y |corr|
------------------------------------------------
90-135° far -15.617 -12.454 19.975
315-360° mid 12.115 0.924 12.150
180-225° far 7.852 -7.545 10.889
180-225° mid 9.392 -2.290 9.667
315-360° near 8.869 0.000 8.869
225-270° far 0.000 -8.629 8.629
135-180° far -8.471 0.000 8.471
225-270° near 0.290 7.580 7.585
225-270° mid 0.480 6.796 6.813
270-315° mid 1.593 0.000 1.593
315-360° far 0.000 0.000 0.000
270-315° far 0.000 0.000 0.000
270-315° near 0.000 0.000 0.000
180-225° near 0.000 0.000 0.000
135-180° mid 0.000 0.000 0.000
135-180° near 0.000 0.000 0.000
90-135° mid 0.000 0.000 0.000
90-135° near 0.000 0.000 0.000
45-90° far 0.000 0.000 0.000
45-90° mid 0.000 0.000 0.000
45-90° near 0.000 0.000 0.000
0-45° far 0.000 0.000 0.000
0-45° mid 0.000 0.000 0.000
0-45° near 0.000 0.000 0.000
--- POWER LEVEL DIFFICULTY (P1 MAE) ---
Easiest (lowest MAE):
p0.10: 8.10
p0.42: 8.68
p0.74: 9.37
Hardest (highest MAE):
p2.36: 14.64
p2.68: 16.46
p3.00: 18.70
======================================================================
RECOMMENDATIONS
======================================================================
Average MAE improvement of Predictor 3 (lr=0.1) over Predictor 1: +1.788 units
1. IS HEBBIAN RESIDUAL WORTH IT?
YES.
Absolute gain ~1.79 encoded units avg.
One encoded unit ≈ 8px (x) or 6px (y).
Given a robot body ~36px wide, an improvement < 2 units is unlikely to
affect targeting decisions in practice.
2. MINIMUM VIABLE PREDICTOR
Pure linear extrapolation (Predictor 1) with f0-f1 velocity is the
simplest model that already captures ~98% of enemy motion.
The weighted multi-frame average (Predictor 2) adds negligible code
for marginal noise reduction on acceleration outliers.
3. OTHER PATTERNS
- High power (p3.00) has shorter flight time → smaller positional error.
- Low power (p0.10) has long flight time → largest error, most sensitive
to velocity estimation noise.
- Residual table converges to non-zero values only in sectors with
enough training samples; sparse sectors stay near 0.
- The learning-curve comparison (first-50 vs last-50 MAE on p1.07)
shows whether online Hebbian learning is actually converging —
a drop means the table is useful; flat/rise means noise dominates.