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
This commit is contained in:
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"""
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Multi-enemy comparison: Linear+Hebbian vs WiSARD K=12 vs TsetlinBot (RTM).
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Uses 181-col decimal CSVs (4 enemies: crazy, spinbot, target, walls).
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Online learning, all predictors share state across battles of the same enemy.
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Rep power: p1.07 for learning signal; all powers reported in summary.
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"""
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import csv, math, os, random, glob
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from collections import defaultdict
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DATA_DIR = os.path.join(os.path.dirname(__file__), "../data")
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OUT_PATH = os.path.join(os.path.dirname(__file__), "battle_comparison.txt")
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POWER_LEVELS = [0.10, 0.42, 0.74, 1.07, 1.39, 1.71, 2.03, 2.36, 2.68, 3.00]
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POWER_STRS = ["p0.10","p0.42","p0.74","p1.07","p1.39","p1.71","p2.03","p2.36","p2.68","p3.00"]
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REP_PS, REP_POWER = "p1.07", 1.07
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MAX_DIST = 1414.0
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HIT_RADIUS = 18.0 # approx half tank width in pixels (0-99 scale ≈ arena/14)
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# ── encoding ───────────────────────────────────────────────────────────────────
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def to_bits(v, n):
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g = int(v) ^ (int(v) >> 1)
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return [(g >> (n - 1 - i)) & 1 for i in range(n)]
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def row_to_binary(row, n_frames=4):
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"""Build n_frames*69-bit input. 181-col CSVs have x/y as 0-99 int."""
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bits = []
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for fi in range(n_frames):
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p = f"f{fi}_"
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bits += to_bits(int(row[p+"bearing_sin"]), 8)
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bits += to_bits(int(row[p+"bearing_cos"]), 8)
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bits += to_bits(int(row[p+"distance"]), 7)
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bits += to_bits(int(row[p+"velocity"]), 5)
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bits += to_bits(int(row[p+"heading_sin"]), 8)
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bits += to_bits(int(row[p+"heading_cos"]), 8)
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bits += to_bits(int(row[p+"enemy_x"]), 7)
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bits += to_bits(int(row[p+"enemy_y"]), 7)
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bits += to_bits(min(int(row[p+"enemy_energy"]), 2046), 11)
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return bits # 4*69 = 276 bits
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# ── WiSARD K=12, 276 bits ──────────────────────────────────────────────────────
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class WiSARD:
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def __init__(self, n_bits=276, k=12, seed=42):
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self.k = k
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n_pad = math.ceil(n_bits / k) * k
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self.n_nodes = n_pad // k
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rng = random.Random(seed)
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idx = list(range(n_bits)) + [0] * (n_pad - n_bits)
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rng.shuffle(idx)
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self.perm = idx
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self.tables = [{} for _ in range(self.n_nodes)] # sparse dicts
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def _addrs(self, bits):
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out = []
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for nd in range(self.n_nodes):
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a = 0
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for b in range(self.k):
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pi = nd * self.k + b
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a = (a << 1) | (bits[self.perm[pi]] if pi < len(bits) else 0)
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out.append(a)
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return out
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def predict(self, bits):
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addrs = self._addrs(bits)
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sx = sy = cnt = 0.0
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for nd, a in enumerate(addrs):
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e = self.tables[nd].get(a)
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if e and e[2] > 0:
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sx += e[0] / e[2]; sy += e[1] / e[2]; cnt += 1
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return (sx/cnt, sy/cnt) if cnt else (0.0, 0.0)
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def learn(self, bits, dx, dy):
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for nd, a in enumerate(self._addrs(bits)):
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e = self.tables[nd].get(a)
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if e is None:
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self.tables[nd][a] = [dx, dy, 1]
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else:
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e[0] += dx; e[1] += dy; e[2] += 1
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# ── Regression TM (one output dimension) ──────────────────────────────────────
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N_CLAUSES = 60
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N_STATES = 15
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S_SPEC = 3.0
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T_THRESH = 30
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RESID_MAX = 30.0 # residual clamp (0-99 scale)
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class RTM:
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def __init__(self):
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n_lit = 276 * 2
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half = N_CLAUSES // 2
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self.ta = [[N_STATES] * n_lit for _ in range(N_CLAUSES)]
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self.pol = [1] * half + [-1] * half
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self.n_lit = n_lit
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def _clause_out(self, c, x_aug):
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ta_c = self.ta[c]
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for l in range(self.n_lit):
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if ta_c[l] > N_STATES and x_aug[l] == 0:
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return 0
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return 1
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def predict(self, x):
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x_aug = x + [1 - b for b in x]
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v = sum(self.pol[c] * self._clause_out(c, x_aug) for c in range(N_CLAUSES))
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v = max(-T_THRESH, min(T_THRESH, v))
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return v / T_THRESH * RESID_MAX
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def learn(self, x, residual):
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x_aug = x + [1 - b for b in x]
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err = residual - self.predict(x)
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p_fb = min(1.0, abs(err) / (2 * RESID_MAX))
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for c in range(N_CLAUSES):
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if random.random() >= p_fb:
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continue
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pol = self.pol[c]; o = self._clause_out(c, x_aug); ta_c = self.ta[c]
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if (err > 0 and pol > 0) or (err < 0 and pol < 0):
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if o == 1:
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for l in range(self.n_lit):
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if x_aug[l] == 1:
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if random.random() < (S_SPEC-1)/S_SPEC:
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if ta_c[l] < 2*N_STATES: ta_c[l] += 1
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else:
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if random.random() < 1.0/S_SPEC:
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if ta_c[l] > 1: ta_c[l] -= 1
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else:
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for l in range(self.n_lit):
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if random.random() < 1.0/S_SPEC:
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if ta_c[l] > 1: ta_c[l] -= 1
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else:
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if o == 1:
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for l in range(self.n_lit):
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if x_aug[l] == 0 and ta_c[l] > N_STATES:
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ta_c[l] -= 1
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# ── helpers ────────────────────────────────────────────────────────────────────
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def bullet_speed(p): return 20.0 - 3.0 * p
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def flight_ticks(dist_enc, p): return (dist_enc / 99.0 * MAX_DIST) / bullet_speed(p)
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def euclid(ax, ay, bx, by): return math.sqrt((ax-bx)**2 + (ay-by)**2)
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def predict_linear(row, power):
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t = flight_ticks(row["f0_distance"], power)
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x0, y0 = row["f0_enemy_x"], row["f0_enemy_y"]
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x1, y1 = row["f1_enemy_x"], row["f1_enemy_y"]
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return x0 + (x0-x1)*t, y0 + (y0-y1)*t
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def heading_sector(hs, hc):
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s = hs/199*2-1; c = hc/199*2-1
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return int(math.degrees(math.atan2(s,c)) % 360 / 45) % 8
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def dist_band(d, lo, hi):
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return 0 if d <= lo else (1 if d <= hi else 2)
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def stats(errs):
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if not errs: return None
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n = len(errs)
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mae = sum(errs)/n
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rmse = math.sqrt(sum(e*e for e in errs)/n)
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hit = sum(1 for e in errs if e < HIT_RADIUS)/n*100
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return {"n":n, "mae":mae, "rmse":rmse, "hit":hit}
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_REQUIRED_KEYS = (
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[f"f{fi}_{col}" for fi in range(4) for col in
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("bearing_sin","bearing_cos","distance","velocity","heading_sin","heading_cos","enemy_x","enemy_y","enemy_energy")]
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+ [f"{ps}_enemy_x" for ps in ["p0.10","p0.42","p0.74","p1.07","p1.39","p1.71","p2.03","p2.36","p2.68","p3.00"]]
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+ [f"{ps}_enemy_y" for ps in ["p0.10","p0.42","p0.74","p1.07","p1.39","p1.71","p2.03","p2.36","p2.68","p3.00"]]
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)
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def load_csv(path):
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rows = []
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with open(path) as f:
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for row in csv.DictReader(f):
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try:
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d = {k: float(v) for k,v in row.items() if v not in (None, "NA", "")}
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if all(k in d for k in _REQUIRED_KEYS):
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rows.append(d)
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except (ValueError, TypeError):
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pass
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return rows
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# ── per-enemy backtest ─────────────────────────────────────────────────────────
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def run_enemy(enemy_files, seed=42):
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"""
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Process all battles for one enemy type.
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Predictors share state across battles (cumulative online learning).
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Returns dict of results.
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"""
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random.seed(seed)
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# shared predictor state
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ws = WiSARD(seed=seed)
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rtm_x, rtm_y = RTM(), RTM()
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heb = [[[0.0, 0.0] for _ in range(3)] for _ in range(8)]
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# compute global band thresholds across all battles
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all_dists = []
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all_rows_list = []
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for path in sorted(enemy_files):
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rows = load_csv(path)
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all_rows_list.append(rows)
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all_dists.extend(r["f0_distance"] for r in rows)
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all_dists.sort()
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n_d = len(all_dists)
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band_lo, band_hi = all_dists[n_d//3], all_dists[2*n_d//3]
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errs = {
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"lin": defaultdict(list),
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"wis": defaultdict(list),
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"tm": defaultdict(list),
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}
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errs_last50_tm = defaultdict(list)
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all_processed = 0
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for rows in all_rows_list:
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n = len(rows)
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for i, row in enumerate(rows):
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x = row_to_binary(row)
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sec = heading_sector(row["f0_heading_sin"], row["f0_heading_cos"])
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band = dist_band(row["f0_distance"], band_lo, band_hi)
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cx_heb, cy_heb = heb[sec][band]
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cx_ws, cy_ws = ws.predict(x)
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rx_tm = rtm_x.predict(x)
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ry_tm = rtm_y.predict(x)
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rx_acc = ry_acc = 0.0
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for ps, power in zip(POWER_STRS, POWER_LEVELS):
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ax = row[f"{ps}_enemy_x"]; ay = row[f"{ps}_enemy_y"]
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lx, ly = predict_linear(row, power)
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errs["lin"][ps].append(euclid(lx+cx_heb, ly+cy_heb, ax, ay))
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errs["wis"][ps].append(euclid(lx+cx_ws, ly+cy_ws, ax, ay))
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errs["tm"][ps].append( euclid(lx+rx_tm, ly+ry_tm, ax, ay))
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if i >= n - 50:
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errs_last50_tm[ps].append(errs["tm"][ps][-1])
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rx_acc += ax - lx; ry_acc += ay - ly
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# online learning
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rx_mean, ry_mean = rx_acc/len(POWER_STRS), ry_acc/len(POWER_STRS)
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ws.learn(x, rx_mean, ry_mean)
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rtm_x.learn(x, rx_mean); rtm_y.learn(x, ry_mean)
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lx_r, ly_r = predict_linear(row, REP_POWER)
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ax_r = row[f"{REP_PS}_enemy_x"]; ay_r = row[f"{REP_PS}_enemy_y"]
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heb[sec][band][0] += 0.1 * (ax_r - (lx_r + cx_heb))
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heb[sec][band][1] += 0.1 * (ay_r - (ly_r + cy_heb))
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all_processed += 1
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return errs, errs_last50_tm, all_processed
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# ── main ──────────────────────────────────────────────────────────────────────
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def main():
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random.seed(42)
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# Find all 181-col decimal CSVs grouped by enemy type
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all_files = sorted(glob.glob(os.path.join(DATA_DIR, "*_decimal.csv")))
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by_enemy = defaultdict(list)
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for f in all_files:
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base = os.path.basename(f)
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enemy = base.split("_battle")[0].replace("_decimal","") # shouldn't be needed but safe
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by_enemy[enemy].append(f)
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lines = []
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w = lines.append
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w("=" * 72)
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w("PREDICTOR COMPARISON: BNNBot (Linear+Hebbian) vs WiSARD K=12 vs TsetlinBot (RTM)")
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w(f"Rep power: {REP_PS} | Hit threshold: {HIT_RADIUS} (0-99 scale, ~18px)")
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w(f"Online learning, cumulative per-enemy | Scores measured across all powers")
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w("=" * 72)
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w("")
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global_errs = {"lin": [], "wis": [], "tm": [], "tm_warm": []}
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enemy_summaries = {}
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for enemy in sorted(by_enemy.keys()):
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files = by_enemy[enemy]
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print(f" {enemy}: {len(files)} battles...")
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errs, errs_last50_tm, n_rows = run_enemy(files)
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# avg MAE across all powers
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def avg_mae(d): return sum(stats(d[ps])["mae"] for ps in POWER_STRS)/len(POWER_STRS)
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def avg_hit(d): return sum(stats(d[ps])["hit"] for ps in POWER_STRS)/len(POWER_STRS)
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s_lin = {"mae": avg_mae(errs["lin"]), "hit": avg_hit(errs["lin"])}
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s_wis = {"mae": avg_mae(errs["wis"]), "hit": avg_hit(errs["wis"])}
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s_tm = {"mae": avg_mae(errs["tm"]), "hit": avg_hit(errs["tm"])}
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s_tm_warm = {
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"mae": sum(stats(errs_last50_tm[ps])["mae"] for ps in POWER_STRS)/len(POWER_STRS)
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if errs_last50_tm["p1.07"] else float("nan"),
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}
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enemy_summaries[enemy] = (s_lin, s_wis, s_tm, s_tm_warm)
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winner = min([("BNNBot(Lin+Heb)", s_lin["mae"]),
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("WiSARD", s_wis["mae"]),
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("TsetlinBot", s_tm["mae"])], key=lambda x:x[1])[0]
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# accumulate globals
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for ps in POWER_STRS:
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global_errs["lin"].extend(errs["lin"][ps])
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global_errs["wis"].extend(errs["wis"][ps])
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global_errs["tm"].extend(errs["tm"][ps])
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global_errs["tm_warm"].extend(errs_last50_tm[ps])
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w(f"Enemy: {enemy:12s} battles={len(files)} rows={n_rows}")
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w(f" Avg MAE (all powers): BNNBot={s_lin['mae']:5.2f} WiSARD={s_wis['mae']:5.2f} TsetlinBot={s_tm['mae']:5.2f} (TM-warm={s_tm_warm['mae']:5.2f})")
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w(f" Avg Hit% (all powers): BNNBot={s_lin['hit']:5.1f}% WiSARD={s_wis['hit']:5.1f}% TsetlinBot={s_tm['hit']:5.1f}%")
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w(f" Winner by MAE: {winner}")
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w("")
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# per-power detail for this enemy
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w(f" Per-power breakdown (MAE, {enemy}):")
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w(f" {'Power':<8} {'BNNBot':>8} {'WiSARD':>8} {'TsetlinBot':>10} {'Best':>12}")
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w(" " + "-" * 52)
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for ps in POWER_STRS:
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ml = stats(errs["lin"][ps])["mae"]
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mw = stats(errs["wis"][ps])["mae"]
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mt = stats(errs["tm"][ps])["mae"]
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best = min([("BNNBot",ml),("WiSARD",mw),("TsetlinBot",mt)], key=lambda x:x[1])[0]
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w(f" {ps:<8} {ml:>8.2f} {mw:>8.2f} {mt:>10.2f} {best:>12s}")
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w("")
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# global summary
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w("=" * 72)
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w("OVERALL SUMMARY (all enemies, all powers combined)")
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w("=" * 72)
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n_all = len(global_errs["lin"])
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if n_all:
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g_lin = sum(global_errs["lin"])/n_all
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g_wis = sum(global_errs["wis"])/n_all
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g_tm = sum(global_errs["tm"])/n_all
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g_tm_warm = sum(global_errs["tm_warm"])/len(global_errs["tm_warm"]) if global_errs["tm_warm"] else float("nan")
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g_hit_lin = sum(1 for e in global_errs["lin"] if e<HIT_RADIUS)/n_all*100
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g_hit_wis = sum(1 for e in global_errs["wis"] if e<HIT_RADIUS)/n_all*100
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g_hit_tm = sum(1 for e in global_errs["tm"] if e<HIT_RADIUS)/n_all*100
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w(f" n={n_all} data points")
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w(f" MAE: BNNBot={g_lin:.2f} WiSARD={g_wis:.2f} TsetlinBot={g_tm:.2f} (TM warm-phase={g_tm_warm:.2f})")
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w(f" Hit%: BNNBot={g_hit_lin:.1f}% WiSARD={g_hit_wis:.1f}% TsetlinBot={g_hit_tm:.1f}%")
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w("")
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# win counts
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wins = defaultdict(int)
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for enemy, (sl, sw, st, _) in enemy_summaries.items():
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best = min([("BNNBot",sl["mae"]),("WiSARD",sw["mae"]),("TsetlinBot",st["mae"])], key=lambda x:x[1])[0]
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wins[best] += 1
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w(" Wins per enemy type (by avg MAE, all powers):")
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for k, v in sorted(wins.items(), key=lambda x:-x[1]):
|
||||
w(f" {k}: {v}/{len(enemy_summaries)}")
|
||||
w("")
|
||||
|
||||
global_winner = min([("BNNBot(Lin+Heb)", g_lin),
|
||||
("WiSARD K=12", g_wis),
|
||||
("TsetlinBot(RTM)", g_tm)], key=lambda x:x[1])[0]
|
||||
global_winner_warm = min([("BNNBot(Lin+Heb)", g_lin),
|
||||
("WiSARD K=12", g_wis),
|
||||
("TsetlinBot(RTM,warm)", g_tm_warm)], key=lambda x:x[1])[0]
|
||||
w(f" VERDICT (all-rows MAE): {global_winner}")
|
||||
w(f" VERDICT (TM warm-phase): {global_winner_warm}")
|
||||
w("")
|
||||
w("Notes:")
|
||||
w(" - BNNBot: linear extrapolation + 24-cell Hebbian residual table (lr=0.1)")
|
||||
w(" - WiSARD: K=12, 276-bit input (4 frames × 69 bits), 23 LUT nodes, bleach=1")
|
||||
w(" - TsetlinBot (RTM): 60 clauses, N_states=15, s=3.0, T=30, shared x/y RTMs")
|
||||
w(" - TM warm = last-50-rows MAE; accounts for cold-start overhead")
|
||||
w(f" - Hit threshold: {HIT_RADIUS} (0-99 scale units, ~equivalent to tank body)")
|
||||
w(" - Residuals learned on mean across all powers; tested at each power level")
|
||||
w(f" - Enemy types: {', '.join(sorted(enemy_summaries.keys()))}")
|
||||
|
||||
report = "\n".join(lines)
|
||||
print("\n" + report)
|
||||
os.makedirs(os.path.dirname(OUT_PATH), exist_ok=True)
|
||||
with open(OUT_PATH, "w") as f:
|
||||
f.write(report + "\n")
|
||||
print(f"\n[saved to {OUT_PATH}]")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user