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

- 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
2026-09-20 00:59:53 +02:00
parent 254c7dc997
commit 1ed7797cb6
184 changed files with 80149 additions and 21 deletions
@@ -0,0 +1,104 @@
+======================================================================
+REGRESSION TSETLIN MACHINE BACKTEST REPORT
+Rows: 277  Clauses: 60  States: 15  s=3.0  T=30
+Input: 4 frames × 68 bits = 272 features (544 literals)
+======================================================================
+
+--- TM (Regression Tsetlin Machine) ---
+
+Power        MAE    RMSE      Max    %<5u
+------------------------------------------
+p0.10      12.97   18.57    78.47   33.9%
+p0.42      13.45   19.19    81.65   32.5%
+p0.74      14.01   19.92    85.14   32.9%
+p1.07      14.65   20.78    89.08   28.9%
+p1.39      15.38   21.73    93.35   27.4%
+p1.71      16.21   22.81    99.09   26.0%
+p2.03      17.24   24.15   104.46   23.1%
+p2.36      18.42   25.69   111.24   21.7%
+p2.68      20.05   27.68   118.79   20.2%
+p3.00      22.06   30.17   127.54   17.0%
+
+--- P1 (Linear Extrapolation) ---
+
+Power        MAE    RMSE      Max    %<5u
+------------------------------------------
+p0.10       8.10   11.93    61.09   49.8%
+p0.42       8.68   12.70    64.35   49.1%
+p0.74       9.37   13.59    67.94   46.6%
+p1.07      10.18   14.62    72.04   40.1%
+p1.39      11.03   15.72    76.48   38.6%
+p1.71      11.97   16.96    81.92   35.7%
+p2.03      13.22   18.49    87.57   32.5%
+p2.36      14.64   20.24    94.46   29.2%
+p2.68      16.46   22.43   102.17   26.4%
+p3.00      18.70   25.16   111.15   23.5%
+
+--- P3 (Linear + Hebbian, lr=0.1) ---
+
+Power        MAE    RMSE      Max    %<5u
+------------------------------------------
+p0.10       6.43    9.49    41.84   54.9%
+p0.42       6.92   10.09    44.94   52.3%
+p0.74       7.57   10.85    48.39   49.5%
+p1.07       8.34   11.76    52.47   45.8%
+p1.39       9.15   12.77    56.79   39.7%
+p1.71      10.14   13.95    61.80   35.0%
+p2.03      11.37   15.43    67.33   32.1%
+p2.36      12.85   17.18    74.19   29.6%
+p2.68      14.72   19.42    81.71   27.8%
+p3.00      16.98   22.23    90.49   26.7%
+
+--- LEARNING CURVE (TM, p1.07) ---
+
+  First-50 MAE: 35.47
+  Last-50  MAE: 9.37
+  Improvement:  +26.11 (converging)
+
+--- TM vs BASELINES (avg MAE across all power levels) ---
+
+  TM avg MAE (all rows): 16.44
+  P1 avg MAE (all rows): 12.24  (TM delta: +4.21)
+  P3 avg MAE (all rows): 10.45  (TM delta: +6.00)
+  TM avg MAE (last 50):  12.45  (warm TM, best proxy for in-battle perf)
+
+--- PER-POWER TM vs P1 ---
+
+Power      TM MAE   P1 MAE   P3 MAE   vs P1   vs P3
+----------------------------------------------------
+p0.10       12.97     8.10     6.43   +4.87   +6.54
+p0.42       13.45     8.68     6.92   +4.77   +6.53
+p0.74       14.01     9.37     7.57   +4.63   +6.43
+p1.07       14.65    10.18     8.34   +4.46   +6.30
+p1.39       15.38    11.03     9.15   +4.35   +6.23
+p1.71       16.21    11.97    10.14   +4.24   +6.07
+p2.03       17.24    13.22    11.37   +4.02   +5.87
+p2.36       18.42    14.64    12.85   +3.78   +5.57
+p2.68       20.05    16.46    14.72   +3.59   +5.33
+p3.00       22.06    18.70    16.98   +3.37   +5.08
+
+--- ANALYSIS ---
+
+The TM starts cold (zero residual prediction) and converges during the battle.
+The all-rows MAE is dominated by early cold-start rows; last-50 MAE is the
+better proxy for real in-battle performance after warm-up.
+
+Key observations:
+- Learning curve shows strong convergence: first-50 to last-50 MAE drops ~26 units.
+- With only 277 rows, the TM sees 277 training steps total (shared RTM).
+  A real battle (~1000 wave hits) would give ~4x more training signal.
+- The TM learns residual correction on top of linear extrapolation, not raw coords.
+  This is the same structure as P3 (Hebbian residual) but with a more expressive
+  non-linear function approximator.
+- The residual target range is ±30 encoded units. If actual residuals
+  exceed this (they can for far enemies), the TM clips silently.
+  Increase RESID_MAX if coverage is needed.
+
+Architectural finding: TM as raw-coordinate predictor fails badly on 277 rows
+(MAE ~66). TM as residual corrector over linear extrapolation converges fast
+and approaches P1/P3 performance in the warm phase. This matches how P3 works.
+
+Next step: implement in Nim as a residual corrector replacing the Hebbian table,
+using M=100 clauses, N_states=20, s=3.0. Expect to match or beat P3 after ~100
+battle ticks with a 1000-tick battle.
+