Developed analytic 9x9 Go policy network
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Developed analytic 9x9 Go policy network
Agent
Codex
Model
GPT 6 Astra
Started
Nov 3, 3:09 AM
Messages181
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Implemented initial analytic policy network with zero weights.
BuildRan iterative game simulations and analyzed outputs.
VerifyRefined policy with improved liberty propagation and eye preservation.
EditEvaluated network performance across thousands of games.
Verifyimport os, json, numpy as np
os.makedirs("/work", exist_ok=True)
source = r'''import numpy as np
DEFAULTS = dict( capture=9.0, rescue=6.0, attack=1.15, self_atari=7.5, eye=11.0, connection=0.9, contact=1.4, own_territory=1.6, enemy_territory=1.0, territory_threshold=3.0, pass_base=-9.0, pass_per_stone=0.10, pass_white=0.35, own_adj=0.25, own_diag=0.65, own_two=0.40, own_knight=0.12, enemy_adj=0.75, enemy_diag=0.25, enemy_two=0.12, temperature_scale=1.0,)
def build(save, overrides=None): cfg = dict(DEFAULTS) if overrides: cfg.update(overrides)
W = [np.zeros((243 if k == 0 else 512, 512), np.float32) for k in range(6)] B = [np.zeros(512, np.float32) for _ in range(6)] OW = np.zeros((512, 82), np.float32) OB = np.zeros(82, np.float32)
def u(ch, p): return 81*ch+p
adj = [] diag = [] rings = [] for p in range(81): r, c = divmod(p, 9) adj.append([rr*9+cc for rr, cc in [(r-1,c), (r+1,c), (r,c-1), (r,c+1)] if 0 <= rr < 9 and 0 <= cc < 9]) diag.append([rr*9+cc for rr, cc in [(r-1,c-1), (r-1,c+1), (r+1,c-1), (r+1,c+1)] if 0 <= rr < 9 and 0 <= cc < 9]) rings.append([(q, abs(r-q//9), abs(c-q%9)) for q in range(81) if q != p])
# Layer 1: board, exact local liberties, and excess liberties. for p in range(81): W[0][3*p, u(0,p)] = 1 W[0][3*p+1, u(1,p)] = 1 for colour in range(2): for ch, excess in [(2+colour, 0), (4+colour, 1)]: j = u(ch,p) B[0][j] = len(adj[p])-4-excess W[0][3*p+colour,j] += 4 for q in adj[p]: W[0][3*q,j] -= 1 W[0][3*q+1,j] -= 1
# Global counts and colour are carried separately. for p in range(81): W[0][3*p,486] = 1 W[0][3*p+1,487] = 1 W[0][2,488] = 1
for k in range(1,6): for g in [486,487,488]: W[k][g,g] = 1
def carry_board(k): for ch in [0,1]: for p in range(81): W[k][u(ch,p),u(ch,p)] = 1
# Layer 2: liberty evidence along monochromatic connections. # Orthogonally adjacent stones cannot share an immediate liberty. carry_board(1) for p in range(81): for colour in range(2): j = u(2+colour,p) B[1][j] = -21 W[1][u(colour,p),j] = 20 for q in [p]+adj[p]: W[1][u(2+colour,q),j] += 1 W[1][u(4+colour,p),u(4+colour,p)] = 1
# Layer 3: spread evidence of safety through connected stones. # The other channels estimate liberties available after extension. carry_board(2) for p in range(81): for colour in range(2): j = u(2+colour,p) B[2][j] = -100 W[2][u(colour,p),j] = 100 for q in [p]+adj[p]: W[2][u(2+colour,q),j] += 1
j = u(4+colour,p) B[2][j] = len(adj[p]) for q in adj[p]: W[2][u(0,q),j] -= 1 W[2][u(1,q),j] -= 1 W[2][u(2+colour,q),j] += 1
# Layer 4: approximate whole-group atari, self-atari, weak enemies. carry_board(3) for p in range(81): for colour in range(2): j = u(2+colour,p) W[3][u(colour,p),j] = 1 for q in [p]+adj[p]: W[3][u(2+colour,q),j] -= 1
B[3][u(4,p)] = 2 W[3][u(4,p),u(4,p)] = -1
j = u(5,p) W[3][u(1,p),j] = 3 W[3][u(3,p),j] = -1 for q in adj[p]: W[3][u(3,q),j] -= 0.5
# Layer 5: spatial prior, captures, attack, safe rescue, and # capture-aware self-atari suppression. carry_board(4) line_prior = [-1.5, 0.25, 1.2, 0.9, 1.0] for p in range(81): r,c = divmod(p,9) j = u(2,p) B[4][j] = (100 + line_prior[min(r,8-r)] + line_prior[min(c,8-c)]) for q,dr,dc in rings[p]: if dr+dc == 1: a,b = cfg["own_adj"],cfg["enemy_adj"] elif dr == 1 and dc == 1: a,b = cfg["own_diag"],cfg["enemy_diag"] elif dr+dc == 2: a,b = cfg["own_two"],cfg["enemy_two"] elif sorted([dr,dc]) == [1,2]: a,b = cfg["own_knight"],0.0 else: continue W[4][u(0,q),j] += a W[4][u(1,q),j] += b for q in adj[p]: W[4][u(3,q),j] += cfg["capture"] W[4][u(5,q),j] += cfg["attack"]
j = u(3,p) W[4][u(4,p),j] = 1 for q in adj[p]: W[4][u(3,q),j] -= 2
j = u(4,p) W[4][u(4,p),j] = -4 for q in adj[p]: W[4][u(2,q),j] += 1
# Layer 6: combine tactics with connection, contact, territory, # and true-eye features. for p in range(81): j = u(0,p) W[5][u(2,p),j] = 1 W[5][u(3,p),j] = -cfg["self_atari"] W[5][u(4,p),j] = cfg["rescue"]
# Connect stones without repeatedly rewarding filled triangles. j = u(1,p) B[5][j] = -1 for q in adj[p]: W[5][u(0,q),j] = 1 for q in diag[p]: W[5][u(0,q),j] = -0.65
# Avoid unsupported contact with multiple enemy stones. j = u(2,p) B[5][j] = -1 for q in adj[p]: W[5][u(1,q),j] = 1 W[5][u(0,q),j] = -0.75 for q in diag[p]: W[5][u(0,q),j] -= 0.20
# Discourage both filling established territory and hopeless # invasions; urgent tactical bonuses can override either. for colour,ch in [(0,3),(1,4)]: j = u(ch,p) B[5][j] = -cfg["territory_threshold"] for q,dr,dc in rings[p]: if dr+dc == 1: weight = 1.0 elif dr == 1 and dc == 1: weight = 0.65 elif dr+dc == 2: weight = 0.45 elif sorted([dr,dc]) == [1,2]: weight = 0.20 else: continue W[5][u(colour,q),j] += weight W[5][u(1-colour,q),j] -= 1.2*weight
# A fully friendly orthogonal surround, adjusted for false eyes. j = u(5,p) B[5][j] = 1-len(adj[p]) for q in adj[p]: W[5][u(0,q),j] += 1 diagonal_penalty = 1.0 if len(adj[p]) < 4 else 0.5 for q in diag[p]: W[5][u(1,q),j] -= diagonal_penalty
for ch,coefficient in [ (0,1.0), (1,cfg["connection"]), (2,-cfg["contact"]), (3,-cfg["own_territory"]), (4,-cfg["enemy_territory"]), (5,-cfg["eye"]), ]: OW[u(ch,p),p] = coefficient OB[p] = -100
OB[81] = cfg["pass_base"] OW[486,81] = cfg["pass_per_stone"] OW[487,81] = cfg["pass_per_stone"] OW[488,81] = cfg["pass_white"]
OW *= cfg["temperature_scale"] OB *= cfg["temperature_scale"]
tensors = {} for k in range(6): tensors["L%d.W"%(k+1)] = W[k] tensors["L%d.b"%(k+1)] = B[k] tensors["out.W"] = OW tensors["out.b"] = OB for name,array in tensors.items(): assert np.isfinite(array).all() save(name,array) return cfg, tensors'''
with open("/work/policy_design.py", "w") as f: f.write(source)
namespace = {}exec(compile(source, "/work/policy_design.py", "exec"), namespace)config, tensors = namespace["build"](save)
with open("/work/policy_config.json", "w") as f: json.dump(config, f, indent=2)np.savez("/work/policy_initial.npz", **tensors)
print("Saved all 14 tensors.")print("Nonzero parameters:", sum(np.count_nonzero(a) for a in tensors.values()))print("Design: analytic liberty propagation, captures, safe rescues,")print("self-atari avoidance, connection, territory, eyes, and phase-aware passing.")print(json.dumps(config, indent=2))Saved all 14 tensors.Nonzero parameters: 21080Design: analytic liberty propagation, captures, safe rescues,self-atari avoidance, connection, territory, eyes, and phase-aware passing.{ "capture": 9.0, "rescue": 6.0, "attack": 1.15, "self_atari": 7.5, "eye": 11.0, "connection": 0.9, "contact": 1.4, "own_territory": 1.6, "enemy_territory": 1.0, "territory_threshold": 3.0, "pass_base": -9.0, "pass_per_stone": 0.1, "pass_white": 0.35, "own_adj": 0.25, "own_diag": 0.65, "own_two": 0.4, "own_knight": 0.12, "enemy_adj": 0.75, "enemy_diag": 0.25, "enemy_two": 0.12, "temperature_scale": 1.0}{"games":{"strong":64,"weak":32,"random":32}}{"ok": true, "pass": 1, "kind": "games", "per_opponent": {"random": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 42.96875, "mean_length": 96.8125, "stones_captured_per_game": 32.09375, "stones_lost_per_game": 0.53125, "passes_per_game": 1.25, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}, "strong": {"games": 64, "score": 0.578125, "wins": 37, "losses": 27, "score_as_black": 0.375, "score_as_white": 0.78125, "mean_margin": 13.09375, "mean_length": 101.0625, "stones_captured_per_game": 19.609375, "stones_lost_per_game": 16.46875, "passes_per_game": 2.9375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 62, "end_repetition": 2, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.90625, "wins": 29, "losses": 3, "score_as_black": 0.8125, "score_as_white": 1.0, "mean_margin": 22.4375, "mean_length": 96.9375, "stones_captured_per_game": 20.6875, "stones_lost_per_game": 9.5, "passes_per_game": 3.75, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_1/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import os, json, numpy as nppath = "/work/pass_1"games = json.load(open(path+"/games.json"))print(type(games), len(games))if isinstance(games, dict): print(games.keys())else: print(json.dumps(games[0], indent=2)[:6500])boards = np.load(path+"/boards.npy")valid = np.load(path+"/valid.npy")print("board shapes", boards.shape, valid.shape)print("files", os.listdir(path))if isinstance(games, list): for i,g in enumerate(games): if g.get("opponent") == "strong" and g.get("result") == -1: print("LOSS",i,{k:v for k,v in g.items() if k!="moves"}) b = boards[i, np.flatnonzero(valid[i])[-1]].reshape(9,9) print("\n".join("".join({0:".",1:"X",-1:"O"}[int(x)] for x in row) for row in b)) if i > 45: break<class 'list'> 128{ "game": 0, "opponent": "random", "net_color": "black", "result": 1, "margin": 73.5, "end": "two passes", "stones_captured_by_net": 71, "stones_lost_by_net": 0, "moves": [ { "ply": 0, "by": "net", "move": 67, "top5": [ [ 20, 0.0369 ], [ 24, 0.0369 ], [ 56, 0.0369 ], [ 60, 0.0369 ], [ 22, 0.0302 ] ] }, { "ply": 1, "by": "opponent", "move": 65 }, { "ply": 2, "by": "net", "move": 57, "top5": [ [ 56, 0.0752 ], [ 57, 0.0574 ], [ 59, 0.0447 ], [ 60, 0.0355 ], [ 58, 0.0331 ] ] }, { "ply": 3, "by": "opponent", "move": 3 }, { "ply": 4, "by": "net", "move": 24, "top5": [ [ 58, 0.0817 ], [ 56, 0.0754 ], [ 66, 0.0605 ], [ 59, 0.0521 ], [ 12, 0.0472 ] ] }, { "ply": 5, "by": "opponent", "move": 59 }, { "ply": 6, "by": "net", "move": 29, "top5": [ [ 58, 0.1474 ], [ 56, 0.0643 ], [ 66, 0.0516 ], [ 60, 0.0501 ], [ 49, 0.0467 ] ] }, { "ply": 7, "by": "opponent", "move": 63 }, { "ply": 8, "by": "net", "move": 20, "top5": [ [ 58, 0.1331 ], [ 56, 0.058 ], [ 66, 0.0466 ], [ 60, 0.0452 ], [ 47, 0.0452 ] ] }, { "ply": 9, "by": "opponent", "move": 4 }, { "ply": 10, "by": "net", "move": 41, "top5": [ [ 58, 0.1269 ], [ 56, 0.0553 ], [ 66, 0.0444 ], [ 60, 0.0431 ], [ 47, 0.0431 ] ] }, { "ply": 11, "by": "opponent", "move": 72 }, { "ply": 12, "by": "net", "move": 39, "top5": [ [ 58, 0.1245 ], [ 49, 0.067 ], [ 56, 0.0481 ], [ 39, 0.0458 ], [ 60, 0.0423 ] ] }, { "ply": 13, "by": "opponent", "move": 0 }, { "ply": 14, "by": "net", "move": 50, "top5": [ [ 58, 0.1071 ], [ 49, 0.0979 ], [ 38, 0.067 ], [ 47, 0.0548 ], [ 31, 0.0506 ] ] }, { "ply": 15, "by": "opponent", "move": 17 }, { "ply": 16, "by": "net", "move": 60, "top5": [ [ 58, 0.2323 ], [ 60, 0.1256 ], [ 49, 0.0809 ], [ 38, 0.0431 ], [ 48, 0.0367 ] ] }, { "ply": 17, "by": "opponent", "move": 42 }, { "ply": 18, "by": "net", "move": 58, "top5": [ [ 58, 0.4931 ], [ 51, 0.0956 ], [ 68, 0.087 ], [ 33, 0.0477 ], [ 49, 0.0411 ] ] }, { "ply": 19, "by": "opponent", "move": 34 }, { "ply": 20, "by": "net", "move": 68, "top5": [ [ 68, 0.9999 ], [ 51, 0.0 ], [ 33, 0.0 ], [ 40, 0.0 ], [ 49, 0.0 ] ] }, { "ply": 21, "by": "opponent", "move": 44 }, { "ply": 22, "by": "net", "move": 47, "top5": [ [ 51, 0.2161 ], [ 33, 0.215 ], [ 40, 0.0492 ], [ 38, 0.0443 ], [ 47, 0.0409 ] ] }, { "ply": 23, "by": "opponent", "move": 10 }, { "ply": 24, "by": "net", "move": 79, "top5": [ [ 51, 0.2029 ], [ 33, 0.2019 ], [ 56, 0.1212 ], [ 38, 0.0464 ], [ 40, 0.0403 ] ] }, { "ply": 25, "by": "opponent", "move": 16 }, { "ply": 26, "by": "net", "move": 22, "top5": [ [ 51, 0.2063 ], [ 33, 0.2053 ], [ 56, 0.1233 ], [ 38, 0.0472 ], [ 40, 0.041 ] ] }, { "ply": 27, "by": "opponent", "move": 18 }, { "ply": 28, "by": "net", "move": 15, "top5": [ [ 33, 0.2001 ], [ 51, 0.1783 ], [ 56, 0.1066 ], [ 19, 0.0822 ], [ 32, 0.0412 ] ] }, { "ply": 29, "by": "opponent", "move": 13 }, { "ply": 30, board shapes (128, 162, 81) (128, 162)files ['boards.npy', 'summary.json', 'README.txt', 'games.json', 'valid.npy']LOSS 33 {'game': 33, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -8.5, 'end': 'two passes', 'stones_captured_by_net': 9, 'stones_lost_by_net': 8}.X.XXXOO.XXX.XOOOOXXXXXOOO.X.XXXOOOOXX.XOOOOO.XXXOO.OOX.XXOOOOO.XXOO.O.OX.XXOOOO.LOSS 35 {'game': 35, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -14.5, 'end': 'two passes', 'stones_captured_by_net': 3, 'stones_lost_by_net': 10}XX.X.XX.XXXXXX.XX.XOOOXXXXXXOOOXXXXXOO.OOXXXOOOOOOOXOOOOO.OOOOOOOOOOOOOO.OO.XOOX.LOSS 36 {'game': 36, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -9.5, 'end': 'two passes', 'stones_captured_by_net': 6, 'stones_lost_by_net': 18}X.XOOOOOO.XXXOOOXOXXXXXOO.O.XXXXO.OOX.XXOOOOO.XXOOOOOOX.XOO.XX..XXXOOOOOX.XXOOO..LOSS 38 {'game': 38, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': 9.5, 'end': 'repetition', 'stones_captured_by_net': 15, 'stones_lost_by_net': 35}.X.X..OO.OOXXO.OOOOOO.O.OXOOOOOOOOXXXOOOOOXX.XXXOXXX.XX.XOXX.X..XXXXXX.XX.X.X.XX.LOSS 40 {'game': 40, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -21.5, 'end': 'two passes', 'stones_captured_by_net': 4, 'stones_lost_by_net': 25}.X.X.XXO.XXX.X.XOO.XXXXXXOOXXXXXXOOOOOOXXXOOOOOOOOOOOOOO.OO.XOOO.XOOOOOO.OOOOOOO.LOSS 41 {'game': 41, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -8.5, 'end': 'two passes', 'stones_captured_by_net': 10, 'stones_lost_by_net': 18}X.XXXXOO.XX.XXOOOO.XXXXOOO.XX.XXO..O.XXXOOOOOXXXXOOOOOXXOOO.XXX.XOO.OOO.XXXOOOOOOLOSS 42 {'game': 42, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -17.5, 'end': 'two passes', 'stones_captured_by_net': 7, 'stones_lost_by_net': 32}.XXOO.OO.XXOOOXOOOXXOOO.OOO.XXXOO.OOXXXXOOOOOX.XXOO.OO.XXOOOOOOXXXXOOOOX.XXXXXOO.LOSS 44 {'game': 44, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -12.5, 'end': 'two passes', 'stones_captured_by_net': 21, 'stones_lost_by_net': 37}OXXXX.X.XOOOXXX.XXOOOOXXX.XOX.OOXXXXOOOOOOXXX.O.OXXX.XOOOOOXXXXOOO.OOOOX.OOOO.OO.LOSS 45 {'game': 45, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -27.5, 'end': 'two passes', 'stones_captured_by_net': 11, 'stones_lost_by_net': 54}OOOOOOXXX.OOOOXXX.OOOOOOXXX.OOOOXXXXOOOOOXXX.OXXOOOXXXOOX.OXXXXOOOOOXOOXOOOOOOOOOLOSS 47 {'game': 47, 'opponent': 'strong', 'net_color': 'black', 'result': -1, 'margin': -9.5, 'end': 'two passes', 'stones_captured_by_net': 5, 'stones_lost_by_net': 7}XXX.X.X.XX.XXXXXX.XX.XX.XXXOXXXXXXOOOOXXOOOOOOOOOOO.O.OO.O.OXOX.OOOXOOOO.OOOOOOO.import json, numpy as np, collectionsgames=json.load(open("/work/pass_1/games.json"))boards=np.load("/work/pass_1/boards.npy")N=[[] for _ in range(81)]for p in range(81): r,c=divmod(p,9) N[p]=[rr*9+cc for rr,cc in [(r-1,c),(r+1,c),(r,c-1),(r,c+1)] if 0<=rr<9 and 0<=cc<9]def groups(b): seen=set(); out=[] for p in range(81): if not b[p] or p in seen: continue stones={p}; stack=[p]; seen.add(p); libs=set() while stack: q=stack.pop() for z in N[q]: if b[z]==0: libs.add(z) elif b[z]==b[p] and z not in seen: seen.add(z); stones.add(z); stack.append(z) out.append((int(b[p]),stones,libs)) return outdef show(b): print("\n".join("".join(".XO"[{0:0,1:1,-1:2}[int(x)]] for x in row) for row in b.reshape(9,9)))stats=collections.Counter()examples=[]for gi,g in enumerate(games): if g["opponent"]!="strong": continue me=1 if g["net_color"]=="black" else -1 for t,m in enumerate(g["moves"]): before=boards[gi,t-1] if t else np.zeros(81,dtype=np.int8) after=boards[gi,t] if m["by"]=="net": caps=collections.Counter() for col,st,libs in groups(before): if col==-me and len(libs)==1: caps[next(iter(libs))]+=len(st) if caps: stats["capture_available"]+=1 if m["move"] in caps: stats["capture_taken"]+=1 elif not any(a in caps for a,p in m["top5"]): stats["capture_absent_top5"]+=1 else: lost=int(np.sum((before==me)&(after!=me))) if lost>=3: stats["loss_events_3plus"]+=1 prev=g["moves"][t-1] if t else None preprev=boards[gi,t-2] if t>=2 else np.zeros(81,dtype=np.int8) threatened=[(len(st),sorted(libs)) for col,st,libs in groups(preprev) if col==me and len(libs)==1] examples.append((lost,gi,t,prev,threatened,preprev.copy()))print(dict(stats))for lost,gi,t,prev,threatened,b in sorted(examples,reverse=True,key=lambda x:x[0])[:8]: print("\nCAPTURED",lost,"game",gi,"ply",t,"our previous move",prev, "prior own ataris",threatened) show(b){'capture_available': 875, 'capture_taken': 679, 'loss_events_3plus': 85, 'capture_absent_top5': 161}
CAPTURED 17 game 51 ply 81 our previous move {'ply': 80, 'by': 'net', 'move': 9, 'top5': [[9, 0.7707], [28, 0.1547], [0, 0.0437], [81, 0.0302], [79, 0.0003]]} prior own ataris [(1, [1]), (4, [5]), (17, [52])]..XOX.O.O.OOOXXOOXXXOOXOOXXX.XOOOOOX.XXXOXXXXX.XOOXX.XXXXOXXXXOXOOOOXXOOXO.O.OO..
CAPTURED 15 game 83 ply 82 our previous move {'ply': 81, 'by': 'net', 'move': 37, 'top5': [[37, 0.652], [81, 0.3356], [71, 0.007], [75, 0.003], [76, 0.002]]} prior own ataris [(1, [71])]XXXXXOXX.XOOXXOXXXOOOXOOOOO.OXXXOO.OO.OXXOOOOOOOXXO.O.OXOXOOOOOXXOXXXXO..XX..X.XO
CAPTURED 14 game 45 ply 89 our previous move {'ply': 88, 'by': 'net', 'move': 81, 'top5': [[81, 0.6572], [33, 0.3428], [44, 0.0], [72, 0.0], [80, 0.0]]} prior own ataris [(2, [0]), (14, [36])].OOOOOXXXXXOOOXXX.OOOOOOXXXXXXXOX.XX.XXOOXXX.XXXOOOXXXOXXXOXXXXOOXOOXOOX.OXOOO.O.
CAPTURED 14 game 62 ply 81 our previous move {'ply': 80, 'by': 'net', 'move': 79, 'top5': [[79, 0.998], [75, 0.0019], [81, 0.0], [76, 0.0], [25, 0.0]]} prior own ataris [(1, [79])].XOOO.OOOXXXXOOO.O.XXXXOO.OX.XOOXOOOXXXXOXXOX.XXOOOXXXX.XXOOXXX.XXOOOOX.X.X..OX.X
CAPTURED 13 game 91 ply 86 our previous move {'ply': 85, 'by': 'net', 'move': 81, 'top5': [[81, 1.0], [61, 0.0], [79, 0.0], [67, 0.0], [0, 0.0]]} prior own ataris [(13, [12])]XOOOOOOXXXXO.OOXXXXXXOOXXOOX.XXOOXXOXXXXXXXXOXXXOOOOOOXXOOOOO.OXXXO.OOOO.XOOOOO.O
CAPTURED 12 game 48 ply 83 our previous move {'ply': 82, 'by': 'net', 'move': 45, 'top5': [[45, 1.0], [81, 0.0], [56, 0.0], [63, 0.0], [72, 0.0]]} prior own ataris [(12, [0]), (1, [8])].XXXXXOX.OXXXOXOOOOOXXOOOXOOOOXOOXXXOOOOOXXX..XXOXXX.XOO.OOX.X..OOOXXX.X.OOXXXXX.
CAPTURED 11 game 38 ply 81 our previous move {'ply': 80, 'by': 'net', 'move': 0, 'top5': [[0, 1.0], [81, 0.0], [55, 0.0], [52, 0.0], [44, 0.0]]} prior own ataris [].X.XXXOO.OXXXOXOOOOOOXOXOXOOOOOOOOXXXOOOOOXX.XXXOXXX.XX.XOXX.X..XXXXXX.XX.X.X.XX.
CAPTURED 11 game 54 ply 73 our previous move {'ply': 72, 'by': 'net', 'move': 44, 'top5': [[44, 0.5784], [2, 0.42], [10, 0.0011], [18, 0.0002], [9, 0.0001]]} prior own ataris [(2, [2]), (1, [44])].X.XXOOOX..XOOOOOX.X.XXOXXXX.XXOOX.X.X.XOOXX.X.XOOOOOXXXXXO.OOO.X.XO..OOX.XOOO.O.{"positions":[{"board":[".XOOO.OOO","XXXXOOO.O",".XXXXOO.O","X.XOOXOOO","XXXXOXXOX",".XXOOOXXX","X.XXOOXXX",".XXOOOOX.","X.X..OX.X"],"to_move":"black"},{"board":["XXXXXOXX.","XOOXXOXXX","OOOXOOOOO",".OXXXOO.O","O.OXXOOOO","OOOXXO.O.","OXOXOOOOO","XXOXXXXO.",".XX..X.XO"],"to_move":"white"},{"board":[".X.XXXOO.","OXXXOXOOO","OOOXOXOXO","OOOOOOOXX","XOOOOOXX.","XXXOXXX.X","X.XOXX.X.","XXXXXXX.X","X.X.X.XX."],"to_move":"black"}],"activations":true}{"ok": true, "pass": 2, "kind": "positions", "positions": 3, "top3_per_position": [[["(8,7)", 0.998], ["(8,3)", 0.0019], ["pass", 0.0]], [["(8,6)", 1.0], ["(4,1)", 0.0], ["pass", 0.0]], [["(0,0)", 1.0], ["pass", 0.0], ["(5,7)", 0.0]]], "files": "/work/pass_2/", "readme": "inputs.npy (P, 243) the network inputs; legal.npy (P, 82) the legal moves (for 'board' positions: the empty points and pass; suicide and ko are not checked); logits.npy, probs.npy (P, 82); act_L1.npy .. act_L6.npy (P, 512) the hidden activations after ReLU."}import numpy as nppath="/work/pass_2/"X=np.load(path+"inputs.npy").reshape(-1,81,3)logits=np.load(path+"logits.npy")acts=[np.load(path+"act_L%d.npy"%k) for k in range(1,7)]for n, candidates in enumerate([[79,75,76],[37,78,71],[0,55,52]]): print("\nPOSITION",n) for p in candidates: print("move",divmod(p,9),"logit",float(logits[n,p])) for k in range(6): print(" L",k+1, np.round(acts[k][n,:486].reshape(6,81)[:,p],3).tolist()) r,c=divmod(p,9) nn=[rr*9+cc for rr,cc in [(r-1,c),(r+1,c),(r,c-1),(r,c+1)] if 0<=rr<9 and 0<=cc<9] print("adjacent stone features:", [(q,X[n,q,:2].astype(int).tolist(), np.round(acts[3][n,:486].reshape(6,81)[:,q],2).tolist()) for q in nn])
POSITION 0move (8, 7) logit 8.310005187988281 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 2.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 5 [0.0, 0.0, 100.51000213623047, 0.0, 1.0, 0.0] L 6 [106.51000213623047, 2.0, 0.0, 0.0, 0.0, 0.0]adjacent stone features: [(70, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (78, [1, 0], [1.0, 0.0, 1.0, 0.0, 1.0, 0.0]), (80, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]move (8, 3) logit 2.05999755859375 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 2.0, 2.0] L 4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 5 [0.0, 0.0, 102.05999755859375, 0.0, 0.0, 0.0] L 6 [102.05999755859375, 0.0, 0.0, 0.0, 0.0, 0.0]adjacent stone features: [(66, [0, 1], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]), (74, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (76, [0, 0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0])]move (8, 4) logit -5.090003967285156 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 1.0, 2.0] L 4 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 5 [0.0, 0.0, 103.80999755859375, 1.0, 0.0, 0.0] L 6 [96.30999755859375, 0.0, 1.0, 0.0, 0.0, 0.0]adjacent stone features: [(67, [0, 1], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0]), (75, [0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (77, [0, 1], [0.0, 1.0, 0.0, 0.0, 1.0, 1.0])]
POSITION 1move (4, 1) logit -0.9860000610351562 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 2.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 5 [0.0, 0.0, 104.91999816894531, 0.0, 0.0, 0.0] L 6 [104.91999816894531, 1.7000000476837158, 0.0, 1.2100000381469727, 0.0, 0.5]adjacent stone features: [(28, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (46, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (36, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (38, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]move (8, 6) logit 13.819999694824219 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0] L 4 [0.0, 0.0, 0.0, 0.0, 2.0, 0.0] L 5 [0.0, 0.0, 116.33999633789062, 0.0, 0.0, 0.0] L 6 [116.33999633789062, 0.0, 1.7999999523162842, 0.0, 0.0, 0.0]adjacent stone features: [(69, [0, 1], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0]), (77, [0, 1], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]), (79, [0, 1], [0.0, 1.0, 0.0, 1.0, 1.0, 3.0])]move (7, 8) logit -5.525001525878906 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 5 [0.0, 0.0, 100.76000213623047, 1.0, 0.0, 0.0] L 6 [93.26000213623047, 1.350000023841858, 0.0, 0.0, 0.0, 0.0]adjacent stone features: [(62, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (80, [1, 0], [1.0, 0.0, 1.0, 0.0, 1.0, 0.0]), (70, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]
POSITION 2move (0, 0) logit 11.339996337890625 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 5 [0.0, 0.0, 111.33999633789062, 0.0, 0.0, 0.0] L 6 [111.33999633789062, 0.0, 0.0, 0.0, 0.0, 0.0]adjacent stone features: [(9, [0, 1], [0.0, 1.0, 0.0, 1.0, 0.0, 3.0]), (1, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]move (6, 1) logit -16.91400146484375 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 1.0, 0.0] L 5 [0.0, 0.0, 105.7699966430664, 1.0, 0.0, 0.0] L 6 [98.2699966430664, 0.4000000059604645, 0.0, 2.8399999141693115, 0.0, 1.0]adjacent stone features: [(46, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (64, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (54, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (56, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]move (5, 7) logit -9.397003173828125 L 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 3 [0.0, 0.0, 0.0, 0.0, 7.0, 0.0] L 4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] L 5 [0.0, 0.0, 104.08000183105469, 0.0, 0.0, 0.0] L 6 [104.08000183105469, 2.3499999046325684, 0.0, 2.869999885559082, 0.0, 1.0]adjacent stone features: [(43, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (61, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (51, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (53, [1, 0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]import json, numpy as npp="/work/policy_design.py"s=open(p).read()s=s.replace('temperature_scale=1.0,','temperature_scale=1.8,\n surrounded_penalty=10.0,')s=s.replace( ' # Layer 6: combine tactics', ''' # Empty points surrounded by friendly stones are normally already # owned. Preserve them even when diagonal enemies make them false eyes. for p in range(81): j = u(5,p) B[4][j] = 1-len(adj[p]) for q in adj[p]: W[4][u(0,q),j] = 1
# Layer 6: combine tactics''')s=s.replace( 'W[5][u(4,p),j] = cfg["rescue"]', 'W[5][u(4,p),j] = cfg["rescue"]\n W[5][u(5,p),j] = -cfg["surrounded_penalty"]')open(p,"w").write(s)ns={}exec(compile(s,p,"exec"),ns)cfg,tensors=ns["build"](save)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v2.npz",**tensors)print("Saved v2: protect all friendly surrounds and sharpen the analytic policy.")Saved v2: protect all friendly surrounds and sharpen the analytic policy.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 3, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6145833134651184, "wins": 59, "losses": 37, "score_as_black": 0.4583333432674408, "score_as_white": 0.7708333134651184, "mean_margin": 14.770833015441895, "mean_length": 99.73958333333333, "stones_captured_per_game": 20.020833333333332, "stones_lost_per_game": 15.770833333333334, "passes_per_game": 2.4479166666666665, "games_with_a_pass_before_move_60": 0, "end_two_passes": 88, "end_repetition": 8, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.9375, "score_as_white": 0.9375, "mean_margin": 34.1875, "mean_length": 97.34375, "stones_captured_per_game": 26.46875, "stones_lost_per_game": 3.125, "passes_per_game": 3.4375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_3/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import os, json, numpy as npos.system("cp /work/policy_design.py /work/policy_design_v2.py")source = r'''import numpy as np
DEFAULTS = dict( capture=24.0, rescue=8.0, attack=1.15, self_atari=7.5, eye=16.0, connection=0.9, contact=1.4, own_territory=1.6, territory_threshold=3.0, pass_base=-9.0, pass_per_stone=0.10, pass_white=0.35, own_adj=0.25, own_diag=0.65, own_two=0.40, own_knight=0.12, enemy_adj=0.75, enemy_diag=0.25, enemy_two=0.12, temperature_scale=1.8,)def build(save, overrides=None): c=dict(DEFAULTS) if overrides: c.update(overrides) W=[np.zeros((243 if k==0 else 512,512),np.float32) for k in range(6)] B=[np.zeros(512,np.float32) for k in range(6)] OW=np.zeros((512,82),np.float32); OB=np.zeros(82,np.float32) def u(ch,p): return 81*ch+p adj=[]; diag=[]; rings=[] for p in range(81): r,s=divmod(p,9) adj.append([a*9+b for a,b in [(r-1,s),(r+1,s),(r,s-1),(r,s+1)] if 0<=a<9 and 0<=b<9]) diag.append([a*9+b for a,b in [(r-1,s-1),(r-1,s+1),(r+1,s-1),(r+1,s+1)] if 0<=a<9 and 0<=b<9]) rings.append([(q,abs(r-q//9),abs(s-q%9)) for q in range(81) if q!=p]) for p in range(81): W[0][3*p,u(0,p)]=1; W[0][3*p+1,u(1,p)]=1 for colour in [0,1]: j=u(2+colour,p) B[0][j]=len(adj[p])-4 W[0][3*p+colour,j]=4 for q in adj[p]: W[0][3*q,j]-=1; W[0][3*q+1,j]-=1 # Contact and connection features, directly from the board. B[0][u(4,p)]=-1; B[0][u(5,p)]=-1 for q in adj[p]: W[0][3*q+1,u(4,p)]+=1 W[0][3*q,u(4,p)]-=0.75 W[0][3*q,u(5,p)]+=1 for q in diag[p]: W[0][3*q,u(4,p)]-=0.20 W[0][3*q,u(5,p)]-=0.65 W[0][3*p,486]=1; W[0][3*p+1,487]=1 W[0][2,488]=1 for k in range(1,6): for g in [486,487,488]: W[k][g,g]=1 def board(k): for col in [0,1]: for p in range(81): W[k][u(col,p),u(col,p)]=1
board(1) line=[-1.5,0.25,1.2,0.9,1.0] for p in range(81): for colour in [0,1]: j=u(2+colour,p) B[1][j]=-21; W[1][u(colour,p),j]=20 for q in [p]+adj[p]: W[1][u(2+colour,q),j]+=1 j=u(4,p); B[1][j]=-c["territory_threshold"] for q,dr,dc in rings[p]: if dr+dc==1: a=1.0 elif dr==dc==1: a=0.65 elif dr+dc==2: a=0.45 elif sorted([dr,dc])==[1,2]: a=0.20 else: continue W[1][u(0,q),j]+=a; W[1][u(1,q),j]-=1.2*a j=u(5,p); r,s=divmod(p,9) B[1][j]=100+line[min(r,8-r)]+line[min(s,8-s)] W[1][u(4,p),j]=-c["contact"] W[1][u(5,p),j]=c["connection"] for q,dr,dc in rings[p]: if dr+dc==1: a,b=c["own_adj"],c["enemy_adj"] elif dr==dc==1: a,b=c["own_diag"],c["enemy_diag"] elif dr+dc==2: a,b=c["own_two"],c["enemy_two"] elif sorted([dr,dc])==[1,2]: a,b=c["own_knight"],0 else: continue W[1][u(0,q),j]+=a; W[1][u(1,q),j]+=b
# Propagate liberty evidence for three more layers. # Conservative analytic upper bounds make every colour gate exact. for k,gate in [(2,100),(3,500)]: board(k) for p in range(81): for colour in [0,1]: j=u(2+colour,p) B[k][j]=-gate; W[k][u(colour,p),j]=gate for q in [p]+adj[p]: W[k][u(2+colour,q),j]+=1 if k==2: j=u(4,p); B[k][j]=2-len(adj[p]) for q in adj[p]: W[k][u(0,q),j]+=1 W[k][u(1,q),j]+=1 W[k][u(2,q),j]-=1 W[k][u(5,p),u(5,p)]=1 W[k][u(4,p),u(5,p)]=-c["own_territory"] else: W[k][u(4,p),u(4,p)]=1 W[k][u(5,p),u(5,p)]=1
# Friendly danger now reaches four links; enemy safety reaches four. board(4) for p in range(81): j=u(2,p); W[4][u(0,p),j]=1 for q in [p]+adj[p]: W[4][u(2,q),j]-=1 j=u(3,p); B[4][j]=-2500; W[4][u(1,p),j]=2500 for q in [p]+adj[p]: W[4][u(3,q),j]+=1 W[4][u(4,p),u(4,p)]=1 W[4][u(5,p),u(5,p)]=1
for p in range(81): W[5][u(5,p),u(0,p)]=1 j=u(1,p) W[5][u(4,p),j]=-4 for q in adj[p]: W[5][u(2,q),j]+=1 j=u(2,p); W[5][u(1,p),j]=1 for q in [p]+adj[p]: W[5][u(3,q),j]-=1 W[5][u(4,p),u(3,p)]=1 j=u(4,p); B[5][j]=1-len(adj[p]) for q in adj[p]: W[5][u(0,q),j]+=1 j=u(5,p); W[5][u(1,p),j]=3 W[5][u(3,p),j]=-1 for q in adj[p]: W[5][u(3,q),j]-=0.5
OW[u(0,p),p]=1 OW[u(1,p),p]=c["rescue"] OW[u(3,p),p]=-c["self_atari"] OW[u(4,p),p]=-c["eye"] for q in adj[p]: OW[u(2,q),p]+=c["capture"] OW[u(5,q),p]+=c["attack"] OB[p]=-100
OB[81]=c["pass_base"] OW[486,81]=OW[487,81]=c["pass_per_stone"] OW[488,81]=c["pass_white"] OW*=c["temperature_scale"]; OB*=c["temperature_scale"] ts={} for k in range(6): ts["L%d.W"%(k+1)]=W[k]; ts["L%d.b"%(k+1)]=B[k] ts["out.W"]=OW; ts["out.b"]=OB for n,a in ts.items(): assert np.isfinite(a).all() save(n,a) return c,ts'''open("/work/policy_design_v3.py","w").write(source)open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v3.npz",**tensors)print("Saved v3: longer-range liberty propagation and conservative eye preservation.")Saved v3: longer-range liberty propagation and conservative eye preservation.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 4, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.5520833134651184, "wins": 53, "losses": 43, "score_as_black": 0.4583333432674408, "score_as_white": 0.6458333134651184, "mean_margin": 12.989583015441895, "mean_length": 102.98958333333333, "stones_captured_per_game": 21.489583333333332, "stones_lost_per_game": 16.979166666666668, "passes_per_game": 3.5625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 88, "end_repetition": 8, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.75, "wins": 24, "losses": 8, "score_as_black": 0.625, "score_as_white": 0.875, "mean_margin": 27.65625, "mean_length": 101.15625, "stones_captured_per_game": 24.6875, "stones_lost_per_game": 7.34375, "passes_per_game": 5.15625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 29, "end_repetition": 3, "end_move_limit": 0}}, "files": "/work/pass_4/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v2.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)config=dict( own_territory=2.5, territory_threshold=1.5, own_adj=0.20, own_diag=0.50, own_two=0.70, own_knight=0.30, enemy_adj=0.70, enemy_diag=0.40, enemy_two=0.25,)cfg,tensors=ns["build"](save,config)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v4.npz",**tensors)print("Saved v4: prioritize outward extension and contested boundaries over interior consolidation.")print(json.dumps(cfg,indent=2))Saved v4: prioritize outward extension and contested boundaries over interior consolidation.{ "capture": 9.0, "rescue": 6.0, "attack": 1.15, "self_atari": 7.5, "eye": 11.0, "connection": 0.9, "contact": 1.4, "own_territory": 2.5, "enemy_territory": 1.0, "territory_threshold": 1.5, "pass_base": -9.0, "pass_per_stone": 0.1, "pass_white": 0.35, "own_adj": 0.2, "own_diag": 0.5, "own_two": 0.7, "own_knight": 0.3, "enemy_adj": 0.7, "enemy_diag": 0.4, "enemy_two": 0.25, "temperature_scale": 1.8, "surrounded_penalty": 10.0}{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 5, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7395833134651184, "wins": 71, "losses": 25, "score_as_black": 0.6666666865348816, "score_as_white": 0.8125, "mean_margin": 24.82291603088379, "mean_length": 102.85416666666667, "stones_captured_per_game": 26.958333333333332, "stones_lost_per_game": 14.479166666666666, "passes_per_game": 2.5208333333333335, "games_with_a_pass_before_move_60": 0, "end_two_passes": 85, "end_repetition": 10, "end_move_limit": 1}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.9375, "score_as_white": 0.9375, "mean_margin": 31.90625, "mean_length": 93.96875, "stones_captured_per_game": 23.90625, "stones_lost_per_game": 4.875, "passes_per_game": 3.28125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_5/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v2.py").read()source=source.replace( " # Global counts and colour are carried separately.", ''' # A zero-liberty indicator lets the next layer distinguish a # duplicated single liberty around a bent chain from independent ones. for p in range(81): for colour in range(2): j=u(4+colour,p) W[0][:,j]=0 B[0][j]=-len(adj[p]) W[0][3*p+colour,j]=1 for q in adj[p]: W[0][3*q,j]+=1 W[0][3*q+1,j]+=1
# Global counts and colour are carried separately.''')source=source.replace( ' W[1][u(4+colour,p),u(4+colour,p)] = 1', ''' W[1][u(4+colour,p),j] -= 1
# Independent supplementary safety certificate. A shared # liberty of two neighbours must lie diagonally from p. j=u(4+colour,p) B[1][j]=-21-len(diag[p]) W[1][u(colour,p),j]=20 for q in [p]+adj[p]: W[1][u(2+colour,q),j]+=1 for q in diag[p]: W[1][u(0,q),j]+=1 W[1][u(1,q),j]+=1''')source=source.replace( ' W[2][u(2+colour,q),j] += 1', ''' W[2][u(2+colour,q),j] += 1 W[2][u(4+colour,q),j] += 1''')open("/work/policy_design_v5.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v5.npz",**tensors)print("Saved v5: safety certificates that do not mistake duplicated liberties for two eyes.")Saved v5: safety certificates that do not mistake duplicated liberties for two eyes.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 6, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7291666865348816, "wins": 70, "losses": 26, "score_as_black": 0.75, "score_as_white": 0.7083333134651184, "mean_margin": 46.80208206176758, "mean_length": 112.75, "stones_captured_per_game": 55.979166666666664, "stones_lost_per_game": 12.447916666666666, "passes_per_game": 1.8645833333333333, "games_with_a_pass_before_move_60": 0, "end_two_passes": 84, "end_repetition": 12, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.90625, "wins": 29, "losses": 3, "score_as_black": 0.9375, "score_as_white": 0.875, "mean_margin": 69.3125, "mean_length": 115.78125, "stones_captured_per_game": 71.78125, "stones_lost_per_game": 3.875, "passes_per_game": 2.125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 29, "end_repetition": 3, "end_move_limit": 0}}, "files": "/work/pass_6/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as np, collectionsfor k in [1,3,4,5,6]: gs=json.load(open("/work/pass_%d/games.json"%k)) print("\nPASS",k) for op in sorted(set(g["opponent"] for g in gs)): a=[g for g in gs if g["opponent"]==op] print(op,dict(collections.Counter((g["end"],g["result"]) for g in a)), "loss margins",[g["margin"] for g in a if g["result"]<0])gs=json.load(open("/work/pass_6/games.json"))bs=np.load("/work/pass_6/boards.npy")for i,g in enumerate(gs): if g["end"]=="repetition" and g["result"]<0: print("\nREPETITION",i,"colour",g["net_color"],"margin",g["margin"]) print(json.dumps(g["moves"][-16:])) t=len(g["moves"])-1 b=bs[i,t-1] print("\n".join("".join({0:".",1:"X",-1:"O"}[int(v)] for v in row) for row in b.reshape(9,9))) if i>40: break
PASS 1random {('two passes', 1): 32} loss margins []strong {('two passes', 1): 36, ('two passes', -1): 26, ('repetition', -1): 1, ('repetition', 1): 1} loss margins [-8.5, -14.5, -9.5, 9.5, -21.5, -8.5, -17.5, -12.5, -27.5, -9.5, -20.5, -22.5, -12.5, -20.5, -19.5, -17.5, -6.5, -3.5, -21.5, -1.5, -24.5, -1.5, -9.5, -1.5, -4.5, -0.5, -2.5]weak {('two passes', 1): 29, ('two passes', -1): 3} loss margins [-0.5, -7.5, -11.5]
PASS 3strong {('two passes', 1): 58, ('two passes', -1): 30, ('repetition', -1): 7, ('repetition', 1): 1} loss margins [-13.5, -5.5, -0.5, -18.5, -9.5, 5.5, 0.5, -25.5, -15.5, -20.5, -15.5, -3.5, -2.5, -5.5, -6.5, -2.5, -7.5, -13.5, -4.5, 10.5, -11.5, -1.5, -29.5, 13.5, -11.5, -10.5, -7.5, -10.5, -0.5, 10.5, -8.5, -0.5, -2.5, -9.5, -6.5, -9.5, 45.5]weak {('two passes', 1): 30, ('two passes', -1): 1, ('repetition', -1): 1} loss margins [-3.5, 18.5]
PASS 4strong {('two passes', 1): 52, ('two passes', -1): 36, ('repetition', -1): 7, ('repetition', 1): 1} loss margins [-13.5, -24.5, -4.5, -22.5, -2.5, -23.5, -10.5, -16.5, -8.5, -13.5, -30.5, 12.5, -12.5, -10.5, -0.5, 15.5, -8.5, -11.5, -9.5, -20.5, -25.5, -31.5, -9.5, 13.5, -33.5, -17.5, -1.5, -9.5, -1.5, 20.5, -18.5, -4.5, -2.5, -4.5, -7.5, -7.5, -6.5, -1.5, -8.5, -11.5, -2.5, 9.5, -2.5]weak {('repetition', -1): 3, ('two passes', -1): 5, ('two passes', 1): 24} loss margins [-15.5, -1.5, -32.5, -1.5, 16.5, -3.5, -1.5, 30.5]
PASS 5strong {('two passes', 1): 70, ('repetition', -1): 9, ('two passes', -1): 15, ('move limit', -1): 1, ('repetition', 1): 1} loss margins [9.5, 37.5, -9.5, -5.5, -50.5, -16.5, -1.5, -11.5, -4.5, -5.5, 11.5, -4.5, -11.5, -7.5, -4.5, -3.5, -4.5, 20.5, -0.5, -4.5, 5.5, -7.5, -7.5, 14.5, 33.5]weak {('two passes', 1): 30, ('two passes', -1): 1, ('repetition', -1): 1} loss margins [-17.5, 19.5]
PASS 6strong {('two passes', 1): 68, ('two passes', -1): 16, ('repetition', 1): 2, ('repetition', -1): 10} loss margins [-2.5, -10.5, -5.5, -0.5, -13.5, -5.5, -2.5, -14.5, -17.5, -12.5, -11.5, -5.5, -15.5, 26.5, -2.5, -1.5, -8.5, 2.5, 11.5, 52.5, -5.5, -0.5, -6.5, 10.5, -2.5, -1.5]weak {('two passes', 1): 29, ('repetition', -1): 3} loss margins [25.5, 27.5, 30.5]
REPETITION 7 colour black margin -5.5[{"ply": 55, "by": "opponent", "move": 2}, {"ply": 56, "by": "net", "move": 76, "top5": [[78, 0.4573], [76, 0.3751], [18, 0.0691], [62, 0.0527], [16, 0.0257]]}, {"ply": 57, "by": "opponent", "move": 16}, {"ply": 58, "by": "net", "move": 78, "top5": [[78, 1.0], [6, 0.0], [18, 0.0], [62, 0.0], [9, 0.0]]}, {"ply": 59, "by": "opponent", "move": 5}, {"ply": 60, "by": "net", "move": 18, "top5": [[18, 0.5104], [62, 0.3896], [9, 0.0492], [63, 0.0133], [73, 0.0133]]}, {"ply": 61, "by": "opponent", "move": 7}, {"ply": 62, "by": "net", "move": 9, "top5": [[9, 0.7841], [62, 0.1943], [63, 0.0066], [73, 0.0066], [71, 0.0049]]}, {"ply": 63, "by": "opponent", "move": 1}, {"ply": 64, "by": "net", "move": 62, "top5": [[62, 0.9011], [63, 0.0308], [73, 0.0308], [71, 0.0225], [64, 0.0076]]}, {"ply": 65, "by": "opponent", "move": 79}, {"ply": 66, "by": "net", "move": 80, "top5": [[80, 1.0], [53, 0.0], [71, 0.0], [63, 0.0], [73, 0.0]]}, {"ply": 67, "by": "opponent", "move": 3}, {"ply": 68, "by": "net", "move": 53, "top5": [[53, 0.848], [63, 0.0565], [73, 0.0565], [13, 0.015], [64, 0.014]]}, {"ply": 69, "by": "opponent", "move": 71}, {"ply": 70, "by": "net", "move": 62, "top5": [[62, 1.0], [63, 0.0], [73, 0.0], [13, 0.0], [64, 0.0]]}].OOOOO.O.XO...OXO.XOOO.OOOOXOXOOO.O..XXXOXOOOX.XXOXXO..X.XOXOO...X.XXXXO...XX.X.X
REPETITION 9 colour black margin -0.5[{"ply": 41, "by": "opponent", "move": 2}, {"ply": 42, "by": "net", "move": 28, "top5": [[28, 0.3706], [1, 0.1466], [25, 0.1171], [10, 0.115], [44, 0.0935]]}, {"ply": 43, "by": "opponent", "move": 63}, {"ply": 44, "by": "net", "move": 44, "top5": [[1, 0.2094], [10, 0.1718], [25, 0.1672], [44, 0.1335], [41, 0.1001]]}, {"ply": 45, "by": "opponent", "move": 46}, {"ply": 46, "by": "net", "move": 42, "top5": [[42, 1.0], [1, 0.0], [10, 0.0], [25, 0.0], [41, 0.0]]}, {"ply": 47, "by": "opponent", "move": 54}, {"ply": 48, "by": "net", "move": 41, "top5": [[41, 0.9996], [50, 0.0002], [1, 0.0001], [10, 0.0001], [3, 0.0]]}, {"ply": 49, "by": "opponent", "move": 50}, {"ply": 50, "by": "net", "move": 66, "top5": [[1, 0.4281], [10, 0.3512], [3, 0.0996], [18, 0.0263], [36, 0.0183]]}, {"ply": 51, "by": "opponent", "move": 43}, {"ply": 52, "by": "net", "move": 42, "top5": [[42, 1.0], [1, 0.0], [10, 0.0], [25, 0.0], [56, 0.0]]}, {"ply": 53, "by": "opponent", "move": 18}, {"ply": 54, "by": "net", "move": 41, "top5": [[41, 1.0], [10, 0.0], [1, 0.0], [56, 0.0], [3, 0.0]]}, {"ply": 55, "by": "opponent", "move": 43}, {"ply": 56, "by": "net", "move": 42, "top5": [[42, 1.0], [10, 0.0], [1, 0.0], [25, 0.0], [56, 0.0]]}]..O.X.X..O.XX.X.X.OXX.XXX...XXXOOOX..OOOO..OX.O.OOOOXXO...O..O.O..X.OOO..........
REPETITION 19 colour black margin -2.5[{"ply": 37, "by": "opponent", "move": 26}, {"ply": 38, "by": "net", "move": 38, "top5": [[38, 0.9654], [17, 0.0346], [44, 0.0], [11, 0.0], [19, 0.0]]}, {"ply": 39, "by": "opponent", "move": 53}, {"ply": 40, "by": "net", "move": 17, "top5": [[17, 0.994], [44, 0.006], [61, 0.0], [62, 0.0], [66, 0.0]]}, {"ply": 41, "by": "opponent", "move": 16}, {"ply": 42, "by": "net", "move": 61, "top5": [[44, 0.5232], [61, 0.2266], [62, 0.1008], [66, 0.0537], [11, 0.0469]]}, {"ply": 43, "by": "opponent", "move": 70}, {"ply": 44, "by": "net", "move": 44, "top5": [[44, 1.0], [66, 0.0], [11, 0.0], [65, 0.0], [56, 0.0]]}, {"ply": 45, "by": "opponent", "move": 26}, {"ply": 46, "by": "net", "move": 62, "top5": [[62, 0.6726], [25, 0.3274], [66, 0.0], [11, 0.0], [65, 0.0]]}, {"ply": 47, "by": "opponent", "move": 71}, {"ply": 48, "by": "net", "move": 25, "top5": [[25, 1.0], [66, 0.0], [11, 0.0], [65, 0.0], [56, 0.0]]}, {"ply": 49, "by": "opponent", "move": 64}, {"ply": 50, "by": "net", "move": 24, "top5": [[24, 0.8907], [56, 0.0572], [65, 0.0389], [66, 0.0077], [11, 0.0043]]}, {"ply": 51, "by": "opponent", "move": 26}, {"ply": 52, "by": "net", "move": 25, "top5": [[25, 1.0], [56, 0.0], [65, 0.0], [66, 0.0], [11, 0.0]]}].......O....XXOOOX..XOOO..O.X.XXOOOX.XXXOXOXX...XOXXX..X.XOOOXX.O..OO.OO.........
REPETITION 44 colour black margin -5.5[{"ply": 75, "by": "opponent", "move": 63}, {"ply": 76, "by": "net", "move": 54, "top5": [[54, 1.0], [5, 0.0], [3, 0.0], [73, 0.0], [10, 0.0]]}, {"ply": 77, "by": "opponent", "move": 19}, {"ply": 78, "by": "net", "move": 10, "top5": [[5, 0.3382], [10, 0.2825], [18, 0.2359], [3, 0.1148], [11, 0.0272]]}, {"ply": 79, "by": "opponent", "move": 5}, {"ply": 80, "by": "net", "move": 3, "top5": [[18, 0.6525], [3, 0.3475], [2, 0.0], [11, 0.0], [12, 0.0]]}, {"ply": 81, "by": "opponent", "move": 73}, {"ply": 82, "by": "net", "move": 18, "top5": [[18, 0.6192], [74, 0.3808], [43, 0.0], [2, 0.0], [11, 0.0]]}, {"ply": 83, "by": "opponent", "move": 43}, {"ply": 84, "by": "net", "move": 74, "top5": [[74, 1.0], [2, 0.0], [53, 0.0], [1, 0.0], [12, 0.0]]}, {"ply": 85, "by": "opponent", "move": 12}, {"ply": 86, "by": "net", "move": 11, "top5": [[11, 1.0], [2, 0.0], [75, 0.0], [1, 0.0], [53, 0.0]]}, {"ply": 87, "by": "opponent", "move": 53}, {"ply": 88, "by": "net", "move": 75, "top5": [[75, 0.9768], [2, 0.0106], [0, 0.006], [1, 0.0038], [9, 0.0023]]}, {"ply": 89, "by": "opponent", "move": 73}, {"ply": 90, "by": "net", "move": 74, "top5": [[74, 1.0], [2, 0.0], [0, 0.0], [1, 0.0], [9, 0.0]]}]...XOOOO..XX.XXXOOX.XXXOOO..X.XOXOOOXXXXOX.O.X.XXOOOOOXXXOOXXO..XOOOO.OOXO..O.OO.import json, numpy as npsource=open("/work/policy_design_v5.py").read()source=source.replace( " # Layer 4: approximate whole-group atari, self-atari, weak enemies.", ''' # The unused opponent-extension channel instead detects exactly # an enemy singleton with one liberty. for p in range(81): j=u(5,p) W[2][:,j]=0 B[2][j]=-len(adj[p]) W[2][u(1,p),j]=2 for q in adj[p]: W[2][u(0,q),j]+=1 W[2][u(1,q),j]-=1
# Layer 4: approximate whole-group atari, self-atari, weak enemies.''')source=source.replace( " # Layer 5: spatial prior, captures, attack, safe rescue, and", ''' # Avoid isolated singleton recaptures with at most one other # liberty. These are common ko/snapback repetition traps. for p in range(81): j=u(5,p) W[3][:,j]=0 B[3][j]=-len(adj[p]) for q in adj[p]: W[3][u(5,q),j]+=2 W[3][u(0,q),j]-=3 W[3][u(1,q),j]+=1
# Layer 5: spatial prior, captures, attack, safe rescue, and''')source=source.replace( ' W[4][u(5,q),j] += cfg["attack"]', ' W[4][u(5,p),j] -= cfg.get("repeat_risk",18.0)')# The replacement above puts a single risk term after the adjacency loop.source=source.replace("temperature_scale=1.8,", "temperature_scale=1.8,\n repeat_risk=18.0,")cfg=json.load(open("/work/policy_config.json"))cfg.update(capture=12.5, repeat_risk=18.0)open("/work/policy_design_v6.py","w").write(source)open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v6.npz",**tensors)print("Saved v6: explicit singleton ko/snapback risk avoidance.")Saved v6: explicit singleton ko/snapback risk avoidance.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 7, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6145833134651184, "wins": 59, "losses": 37, "score_as_black": 0.5416666865348816, "score_as_white": 0.6875, "mean_margin": 33.47916793823242, "mean_length": 111.19791666666667, "stones_captured_per_game": 48.625, "stones_lost_per_game": 14.03125, "passes_per_game": 2.0, "games_with_a_pass_before_move_60": 0, "end_two_passes": 92, "end_repetition": 4, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.9375, "score_as_white": 0.9375, "mean_margin": 65.65625, "mean_length": 120.1875, "stones_captured_per_game": 75.625, "stones_lost_per_game": 4.4375, "passes_per_game": 2.5, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_7/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v5.py").read()source=source.replace( " # Layer 4: approximate whole-group atari, self-atari, weak enemies.", ''' # Exact one-stone capture detector in a previously unused channel. for p in range(81): j=u(5,p) W[2][:,j]=0 B[2][j]=-len(adj[p]) W[2][u(1,p),j]=2 for q in adj[p]: W[2][u(0,q),j]+=1 W[2][u(1,q),j]-=1
# Layer 4: approximate whole-group atari, self-atari, weak enemies.''')patch=r''' # At layer 4, encode own/empty/enemy as 1, 1/2, 0. This frees # a full channel for repetition-risk detection without losing attack. for p in range(81): old_weak=W[3][:,u(5,p)].copy() old_bias=B[3][u(5,p)] W[3][:,u(1,p)]=old_weak B[3][u(1,p)]=old_bias W[3][:,u(0,p)]=0 B[3][u(0,p)]=0.5 W[3][u(0,p),u(0,p)]=0.5 W[3][u(1,p),u(0,p)]=-0.5
j=u(5,p) W[3][:,j]=0 B[3][j]=-len(adj[p]) for q in adj[p]: W[3][u(5,q),j]+=2 W[3][u(0,q),j]-=3 W[3][u(1,q),j]+=1
# Decode the board for the final shape layer. for ch,sign,bias in [(0,2,-1),(1,-2,1)]: j=u(ch,p) W[4][:,j]=0 B[4][j]=bias W[4][u(0,p),j]=sign
# Tactical score only here; move the spatial prior to layer 6. j=u(2,p) W[4][:,j]=0 B[4][j]=100 for q in adj[p]: W[4][u(3,q),j]+=cfg["capture"] W[4][u(1,q),j]+=cfg["attack"] W[4][u(5,p),j]-=cfg.get("repeat_risk",18.0)
j=u(5,p) W[4][:,j]=0 B[4][j]=1-2*len(adj[p]) for q in adj[p]: W[4][u(0,q),j]=2
j=u(0,p) r,c=divmod(p,9) B[5][j]=line_prior[min(r,8-r)]+line_prior[min(c,8-c)] for q,dr,dc in rings[p]: if dr+dc==1: a,b=cfg["own_adj"],cfg["enemy_adj"] elif dr==dc==1: a,b=cfg["own_diag"],cfg["enemy_diag"] elif dr+dc==2: a,b=cfg["own_two"],cfg["enemy_two"] elif sorted([dr,dc])==[1,2]: a,b=cfg["own_knight"],0.0 else: continue W[5][u(0,q),j]+=a W[5][u(1,q),j]+=b'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v7.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(capture=9.0,repeat_risk=18.0)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v7.npz",**tensors)print("Saved v7: packed colour channel preserves weak-group attacks while avoiding ko/snapback traps.")Saved v7: packed colour channel preserves weak-group attacks while avoiding ko/snapback traps.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 8, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7916666865348816, "wins": 76, "losses": 20, "score_as_black": 0.6666666865348816, "score_as_white": 0.9166666865348816, "mean_margin": 45.61458206176758, "mean_length": 114.44791666666667, "stones_captured_per_game": 60.135416666666664, "stones_lost_per_game": 11.947916666666666, "passes_per_game": 2.15625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 91, "end_repetition": 3, "end_move_limit": 2}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 0.9375, "score_as_white": 1.0, "mean_margin": 59.21875, "mean_length": 111.71875, "stones_captured_per_game": 64.09375, "stones_lost_per_game": 3.03125, "passes_per_game": 2.5, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_8/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # Komi-aware territorial ambition: Black needs more than half the # board, while White can consolidate a more modest share. for p in range(81): B[5][u(3,p)]=-cfg.get("black_territory_threshold",0.75) W[5][488,u(3,p)]=( cfg.get("black_territory_threshold",0.75) -cfg.get("white_territory_threshold",1.5))
# Passing is safer when materially ahead, riskier when behind. lead=cfg.get("pass_lead",0.10) OW[486,81]+=lead OW[487,81]-=lead OB[81]-=7.5*lead OW[488,81]+=15.0*lead'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v8.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(black_territory_threshold=0.75,white_territory_threshold=1.5,pass_lead=0.10)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v8.npz",**tensors)print("Saved v8: komi-aware expansion and material-aware passing.")Saved v8: komi-aware expansion and material-aware passing.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 9, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7291666865348816, "wins": 70, "losses": 26, "score_as_black": 0.6875, "score_as_white": 0.7708333134651184, "mean_margin": 42.88541793823242, "mean_length": 116.72916666666667, "stones_captured_per_game": 57.739583333333336, "stones_lost_per_game": 15.84375, "passes_per_game": 3.0729166666666665, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 1, "end_move_limit": 2}, "weak": {"games": 32, "score": 0.90625, "wins": 29, "losses": 3, "score_as_black": 0.875, "score_as_white": 0.9375, "mean_margin": 66.59375, "mean_length": 119.78125, "stones_captured_per_game": 79.625, "stones_lost_per_game": 5.0625, "passes_per_game": 2.875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_9/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as np, collectionsg=json.load(open("/work/pass_9/games.json"))b=np.load("/work/pass_9/boards.npy")print("Loss breakdown",dict(collections.Counter((x["net_color"],x["end"]) for x in g if x["opponent"]=="strong" and x["result"]<0)))for i,x in enumerate(g): if x["opponent"]!="strong" or x["result"]>0 or x["net_color"]!="black": continue print("\nGAME",i,"margin",x["margin"],"lost",x["stones_lost_by_net"], "captured",x["stones_captured_by_net"],"end",x["end"]) for t in [19,39,59,len(x["moves"])-1]: if t>=len(x["moves"]): continue print("after",t+1) bb=b[i,t].reshape(9,9) print("\n".join("".join({0:".",1:"X",-1:"O"}[int(z)] for z in row) for row in bb)) print("last net moves",[m for m in x["moves"][-10:] if m["by"]=="net"]) if i>=10: breakLoss breakdown {('black', 'two passes'): 13, ('black', 'move limit'): 1, ('black', 'repetition'): 1, ('white', 'two passes'): 11}
GAME 6 margin -4.5 lost 22 captured 16 end two passesafter 20...............O.....OXXX....XXOO.....XOO......XOO......XOXX.......O.............after 40....X.......X.XOX...X.XXXO...XXOOOO...XOOXOX...XOOXX....XOXXX....OOOOOO..........after 60..X.X.X.X...X.X.XOO.X.XXXOO.OXXOOOOO.XXOO.O.O..XOO.....XXO...O..XOOOOOOO..X...O..after 109X.X.X.XOOXX.X.XOOO.XX.XXXOOX.XXOOOOOXXXOO.O.OX.XOOOXOOXXXO.XXOO.XOOOOOOOXXX.OOO..last net moves [{'ply': 100, 'by': 'net', 'move': 74, 'top5': [[74, 1.0], [81, 0.0], [75, 0.0], [79, 0.0], [80, 0.0]]}, {'ply': 102, 'by': 'net', 'move': 73, 'top5': [[73, 0.5314], [75, 0.4685], [81, 0.0], [72, 0.0], [79, 0.0]]}, {'ply': 104, 'by': 'net', 'move': 72, 'top5': [[72, 1.0], [6, 0.0], [75, 0.0], [81, 0.0], [79, 0.0]]}, {'ply': 106, 'by': 'net', 'move': 6, 'top5': [[6, 1.0], [81, 0.0], [79, 0.0], [80, 0.0], [75, 0.0]]}, {'ply': 108, 'by': 'net', 'move': 81, 'top5': [[81, 0.7941], [5, 0.2059], [79, 0.0], [80, 0.0], [75, 0.0]]}]
GAME 9 margin -0.5 lost 11 captured 9 end two passesafter 20...........X........XOX......OOOO.....X.OX......OOX.....XXOXX......O.............after 40...X......XXOX....X.XOXOOO..XOOOOXO..OXXOXXX...XOOX.....XXOXX....OOOO............after 60..O..O...OXXO..O..X.XO.OOO..XOOOOXOXX.XXOXXXO.XXOOX.X.XXXXOXXO.OXOOOOXO..........after 91XXOO.O.X..XXOXOOO.X.XOXOOOO.XOOOOXOOX.XXOXXXO.XXOOX.XXXXXXOXXXXOXOOOOX.X.OOOOOOX.last net moves [{'ply': 82, 'by': 'net', 'move': 4, 'top5': [[4, 1.0], [14, 0.0], [7, 0.0], [8, 0.0], [81, 0.0]]}, {'ply': 84, 'by': 'net', 'move': 7, 'top5': [[7, 0.8762], [8, 0.0924], [81, 0.0314], [6, 0.0001], [17, 0.0]]}, {'ply': 86, 'by': 'net', 'move': 13, 'top5': [[13, 0.9869], [8, 0.0114], [81, 0.0013], [22, 0.0004], [17, 0.0]]}, {'ply': 88, 'by': 'net', 'move': 22, 'top5': [[22, 1.0], [4, 0.0], [8, 0.0], [81, 0.0], [6, 0.0]]}, {'ply': 90, 'by': 'net', 'move': 81, 'top5': [[8, 0.8073], [81, 0.1913], [17, 0.001], [6, 0.0004], [72, 0.0]]}]
GAME 10 margin -10.5 lost 28 captured 12 end two passesafter 20............X..........X......XXOO.....XOX....XXXOX.....OOOO.....O...O...........after 40......X.....XOOOX....OXXOO....XXOOX....XOXOX..XXXOXX.X.XOOOOXX...O..OOO..........after 60....O..O....XOOOXX..X.XXOOX...XXOOXX.XOXOOOX..XXXOO.OXOXOOOO..O.XO..OOO..........after 102.X.XOOOOOX.XXOOOO..XX.XXOOOX.XXXOO..XX.XOOOOO.XXXOOOOOXXOOOO.OO.XOXOOOOOXXXXOXX..last net moves [{'ply': 92, 'by': 'net', 'move': 11, 'top5': [[1, 0.8061], [11, 0.1826], [34, 0.008], [26, 0.0028], [17, 0.0005]]}, {'ply': 94, 'by': 'net', 'move': 1, 'top5': [[1, 1.0], [34, 0.0], [26, 0.0], [80, 0.0], [0, 0.0]]}, {'ply': 96, 'by': 'net', 'move': 34, 'top5': [[34, 0.7598], [26, 0.2401], [81, 0.0001], [79, 0.0], [17, 0.0]]}, {'ply': 98, 'by': 'net', 'move': 81, 'top5': [[81, 0.9899], [79, 0.0101], [80, 0.0001], [21, 0.0], [63, 0.0]]}, {'ply': 100, 'by': 'net', 'move': 81, 'top5': [[81, 0.9794], [79, 0.0204], [80, 0.0001], [35, 0.0], [34, 0.0]]}]import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # On an empty 9x9 board, take the central star point. This feature # switches off exactly after either player places the first stone. W[0][:,489]=0 B[0][489]=1 for p in range(81): W[0][3*p,489]=-1 W[0][3*p+1,489]=-1 for k in range(1,6): W[k][489,489]=1 OW[489,40]+=cfg.get("opening_center",8.0)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v9.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(opening_center=8.0,pass_lead=0.0, black_territory_threshold=1.5,white_territory_threshold=1.5)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v9.npz",**tensors)print("Saved v9: claim the center on the first move; retain v7's middlegame policy.")Saved v9: claim the center on the first move; retain v7's middlegame policy.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 10, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6458333134651184, "wins": 62, "losses": 34, "score_as_black": 0.5416666865348816, "score_as_white": 0.75, "mean_margin": 36.19791793823242, "mean_length": 113.26041666666667, "stones_captured_per_game": 52.96875, "stones_lost_per_game": 16.416666666666668, "passes_per_game": 2.09375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 2, "end_move_limit": 1}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 64.625, "mean_length": 117.75, "stones_captured_per_game": 74.875, "stones_lost_per_game": 4.25, "passes_per_game": 3.53125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_10/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # Defend vulnerable groups before their final liberty is taken. # The feature is graded: strongest in atari, weaker with nearby # evidence of additional liberties. for p in range(81): j=u(2,p) W[3][:,j]=0 B[3][j]=0 W[3][u(0,p),j]=3 W[3][u(2,p),j]=-1 for q in adj[p]: W[3][u(2,q),j]-=0.5 W[4][u(4,p),u(4,p)]=-12'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v10.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(rescue=2.2,opening_center=0.0)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v10.npz",**tensors)print("Saved v10: graded preventive defense, with suicidal rescues still suppressed.")Saved v10: graded preventive defense, with suicidal rescues still suppressed.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 11, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6875, "wins": 66, "losses": 30, "score_as_black": 0.625, "score_as_white": 0.75, "mean_margin": 36.65625, "mean_length": 110.58333333333333, "stones_captured_per_game": 51.34375, "stones_lost_per_game": 13.3125, "passes_per_game": 2.2083333333333335, "games_with_a_pass_before_move_60": 0, "end_two_passes": 90, "end_repetition": 4, "end_move_limit": 2}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 1.0, "score_as_white": 0.9375, "mean_margin": 66.25, "mean_length": 115.75, "stones_captured_per_game": 75.59375, "stones_lost_per_game": 1.71875, "passes_per_game": 2.8125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_11/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # Topological pressure: an empty point between enemy stones that # lack diagonal support can separate their groups. for p in range(81): j=u(5,p) W[5][:,j]=0 B[5][j]=-1 for q in adj[p]: W[5][u(1,q),j]+=1 for q in diag[p]: W[5][u(1,q),j]-=1 OW[j,p]=cfg.get("cut",1.5)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v11.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(rescue=6.0,connection=1.8,surrounded_penalty=17.0,cut=1.5)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v11.npz",**tensors)print("Saved v11: stronger own connections and pressure on unsupported enemy cuts.")Saved v11: stronger own connections and pressure on unsupported enemy cuts.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 12, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6770833134651184, "wins": 65, "losses": 31, "score_as_black": 0.6041666865348816, "score_as_white": 0.75, "mean_margin": 40.5625, "mean_length": 112.9375, "stones_captured_per_game": 55.0, "stones_lost_per_game": 15.510416666666666, "passes_per_game": 1.9791666666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 94, "end_repetition": 1, "end_move_limit": 1}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.875, "score_as_white": 1.0, "mean_margin": 63.0, "mean_length": 117.25, "stones_captured_per_game": 77.15625, "stones_lost_per_game": 4.15625, "passes_per_game": 3.4375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_12/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=r'''import numpy as npDEFAULTS=dict( capture=6.45,rescue=6.0,attack=2.0,self_atari=7.5, surrounded_penalty=17.0,connection=0.9,contact=1.4, own_territory=2.5,enemy_territory=1.0,territory_threshold=1.5, pass_base=-9.0,pass_per_stone=0.10,pass_white=0.35, own_adj=0.20,own_diag=0.50,own_two=0.70,own_knight=0.30, enemy_adj=0.70,enemy_diag=0.40,enemy_two=0.25, repeat_risk=18.0,temperature_scale=1.8,)def build(save,overrides=None): c=dict(DEFAULTS) if overrides: c.update(overrides) W=[np.zeros((243 if k==0 else 512,512),np.float32) for k in range(6)] B=[np.zeros(512,np.float32) for k in range(6)] OW=np.zeros((512,82),np.float32); OB=np.zeros(82,np.float32) def u(ch,p): return 81*ch+p adj=[]; diag=[]; rings=[] for p in range(81): r,s=divmod(p,9) adj.append([a*9+b for a,b in [(r-1,s),(r+1,s),(r,s-1),(r,s+1)] if 0<=a<9 and 0<=b<9]) diag.append([a*9+b for a,b in [(r-1,s-1),(r-1,s+1),(r+1,s-1),(r+1,s+1)] if 0<=a<9 and 0<=b<9]) rings.append([(q,abs(r-q//9),abs(s-q%9)) for q in range(81) if q!=p]) for p in range(81): W[0][3*p,u(0,p)]=1; W[0][3*p+1,u(1,p)]=1 for col in [0,1]: j=u(2+col,p); B[0][j]=len(adj[p])-4 W[0][3*p+col,j]=4 for q in adj[p]: W[0][3*q,j]-=1; W[0][3*q+1,j]-=1 B[0][u(4,p)]=-1; B[0][u(5,p)]=-1 for q in adj[p]: W[0][3*q+1,u(4,p)]+=1 W[0][3*q,u(4,p)]-=0.75 W[0][3*q,u(5,p)]+=1 for q in diag[p]: W[0][3*q,u(4,p)]-=0.20 W[0][3*q,u(5,p)]-=0.65 W[0][3*p,486]=1; W[0][3*p+1,487]=1 W[0][2,488]=1 for k in range(1,6): for g in [486,487,488]: W[k][g,g]=1
line=[-1.5,0.25,1.2,0.9,1.0] for p in range(81): for col in [0,1]: W[1][u(col,p),u(col,p)]=1 j=u(2+col,p) B[1][j]=-22; W[1][u(col,p),j]=20 W[1][u(2+col,p),j]=2 for q in adj[p]: W[1][u(2+col,q),j]+=1 # Excess direct liberties, used to correct the doubled centre. W[1][u(2,p),u(4,p)]=1 W[1][u(0,p),u(4,p)]=-1 j=u(5,p); r,s=divmod(p,9) B[1][j]=100+line[min(r,8-r)]+line[min(s,8-s)] W[1][u(4,p),j]=-c["contact"] W[1][u(5,p),j]=c["connection"] for q,dr,dc in rings[p]: if dr+dc==1: a,b=c["own_adj"],c["enemy_adj"] elif dr==dc==1: a,b=c["own_diag"],c["enemy_diag"] elif dr+dc==2: a,b=c["own_two"],c["enemy_two"] elif sorted([dr,dc])==[1,2]: a,b=c["own_knight"],0 else: continue W[1][u(0,q),j]+=a; W[1][u(1,q),j]+=b
# Layer 3: packed colour, two safety fields, extension liberties, # exact singleton-atari detector, and accumulated shape score. for p in range(81): B[2][u(0,p)]=0.5 W[2][u(0,p),u(0,p)]=0.5 W[2][u(1,p),u(0,p)]=-0.5 for col in [0,1]: j=u(1+col,p); B[2][j]=-100 W[2][u(col,p),j]=100 for q in [p]+adj[p]: W[2][u(2+col,q),j]+=1 j=u(3,p); B[2][j]=len(adj[p]) for q in adj[p]: W[2][u(0,q),j]-=1; W[2][u(1,q),j]-=1 W[2][u(2,q),j]+=1; W[2][u(4,q),j]-=1 j=u(4,p); B[2][j]=-len(adj[p]) W[2][u(1,p),j]=2 for q in adj[p]: W[2][u(0,q),j]+=1; W[2][u(1,q),j]-=1 W[2][u(5,p),u(5,p)]=1
# Layer 4 adds another full graph-propagation step. for p in range(81): W[3][u(0,p),u(0,p)]=1 for col in [0,1]: j=u(1+col,p) if col==0: B[3][j]=-1000 W[3][u(0,p),j]=1000 else: W[3][u(0,p),j]=-1000 for q in [p]+adj[p]: W[3][u(1+col,q),j]+=1 j=u(3,p); B[3][j]=2 W[3][u(3,p),j]=-1 j=u(4,p) for q in adj[p]: W[3][u(4,q),j]+=2 W[3][u(0,q),j]-=2 W[3][u(5,p),u(5,p)]=1
# Layer 5: atari reaches four links, with separate graded attack. for p in range(81): W[4][u(0,p),u(0,p)]=1 j=u(1,p); B[4][j]=-19 W[4][u(0,p),j]=20 for q in [p]+adj[p]: W[4][u(1,q),j]-=1
j=u(2,p); B[4][j]=1 W[4][u(0,p),j]=-20 for q in [p]+adj[p]: W[4][u(2,q),j]-=1
j=u(3,p); B[4][j]=3 W[4][u(0,p),j]=-20 W[4][u(2,p),j]=-1 for q in adj[p]: W[4][u(2,q),j]-=0.5
W[4][u(3,p),u(4,p)]=1 W[4][u(5,p),u(5,p)]=1 W[4][u(4,p),u(5,p)]=-c["repeat_risk"]
for p in range(81): j=u(0,p); W[5][u(5,p),j]=1 for q in adj[p]: W[5][u(2,q),j]+=c["capture"] W[5][u(3,q),j]+=c["attack"]
j=u(1,p); W[5][u(4,p),j]=-4 for q in adj[p]: W[5][u(1,q),j]+=1 j=u(2,p); W[5][u(4,p),j]=1 for q in adj[p]: W[5][u(2,q),j]-=2
for ch,sign in [(3,1),(5,-1)]: j=u(ch,p); B[5][j]=-c["territory_threshold"] for q,dr,dc in rings[p]: if dr+dc==1: a=1.0 elif dr==dc==1: a=0.65 elif dr+dc==2: a=0.45 elif sorted([dr,dc])==[1,2]: a=0.20 else: continue W[5][u(0,q),j]+=2*sign*a B[5][j]-=sign*a
j=u(4,p); B[5][j]=1-2*len(adj[p]) for q in adj[p]: W[5][u(0,q),j]+=2 for ch,a in [(0,1),(1,c["rescue"]),(2,-c["self_atari"]), (3,-c["own_territory"]),(4,-c["surrounded_penalty"]), (5,-c["enemy_territory"])]: OW[u(ch,p),p]=a OB[p]=-100 OB[81]=c["pass_base"] OW[486,81]=OW[487,81]=c["pass_per_stone"] OW[488,81]=c["pass_white"] OW*=c["temperature_scale"]; OB*=c["temperature_scale"] ts={} for k in range(6): ts["L%d.W"%(k+1)]=W[k]; ts["L%d.b"%(k+1)]=B[k] ts["out.W"]=OW; ts["out.b"]=OB for n,a in ts.items(): assert np.isfinite(a).all() save(n,a) return c,ts'''open("/work/policy_design_v12.py","w").write(source)open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v12.npz",**tensors)print("Saved v12: packed board representation permits deeper tactical propagation.")Saved v12: packed board representation permits deeper tactical propagation.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 13, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.6458333134651184, "wins": 62, "losses": 34, "score_as_black": 0.7083333134651184, "score_as_white": 0.5833333134651184, "mean_margin": 35.48958206176758, "mean_length": 111.41666666666667, "stones_captured_per_game": 51.458333333333336, "stones_lost_per_game": 14.520833333333334, "passes_per_game": 2.5833333333333335, "games_with_a_pass_before_move_60": 0, "end_two_passes": 92, "end_repetition": 4, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.9375, "score_as_white": 0.9375, "mean_margin": 57.8125, "mean_length": 115.625, "stones_captured_per_game": 67.5625, "stones_lost_per_game": 5.96875, "passes_per_game": 3.53125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_13/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=dict( capture=9.0,rescue=6.0,attack=1.15,self_atari=7.5, eye=11.0,connection=0.9,contact=1.4, own_territory=2.5,enemy_territory=1.0,territory_threshold=1.5, pass_base=-12.0,pass_per_stone=0.10,pass_white=3.35, own_adj=0.20,own_diag=0.50,own_two=0.70,own_knight=0.30, enemy_adj=0.70,enemy_diag=0.40,enemy_two=0.25, temperature_scale=2.5,surrounded_penalty=10.0,repeat_risk=18.0,)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v13.npz",**tensors)print("Saved v13: more decisive shape choices and greater endgame persistence as Black.")Saved v13: more decisive shape choices and greater endgame persistence as Black.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 14, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.8020833134651184, "wins": 77, "losses": 19, "score_as_black": 0.7916666865348816, "score_as_white": 0.8125, "mean_margin": 47.16666793823242, "mean_length": 113.95833333333333, "stones_captured_per_game": 58.541666666666664, "stones_lost_per_game": 11.625, "passes_per_game": 2.0208333333333335, "games_with_a_pass_before_move_60": 0, "end_two_passes": 95, "end_repetition": 0, "end_move_limit": 1}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 1.0, "score_as_white": 0.9375, "mean_margin": 62.84375, "mean_length": 114.875, "stones_captured_per_game": 73.53125, "stones_lost_per_game": 3.625, "passes_per_game": 2.78125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_14/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 15, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.7578125, "wins": 97, "losses": 31, "score_as_black": 0.765625, "score_as_white": 0.75, "mean_margin": 44.671875, "mean_length": 113.25, "stones_captured_per_game": 59.890625, "stones_lost_per_game": 12.796875, "passes_per_game": 2.0546875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 124, "end_repetition": 3, "end_move_limit": 1}}, "files": "/work/pass_15/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=json.load(open("/work/policy_config.json"))cfg.update(enemy_adj=1.3,enemy_diag=0.7,enemy_two=0.4, own_adj=0.4,attack=1.4)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v14.npz",**tensors)print("Saved v14: contest the opponent's boundary earlier instead of conceding central space.")Saved v14: contest the opponent's boundary earlier instead of conceding central space.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 16, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.8020833134651184, "wins": 77, "losses": 19, "score_as_black": 0.7708333134651184, "score_as_white": 0.8333333134651184, "mean_margin": 46.1875, "mean_length": 113.73958333333333, "stones_captured_per_game": 57.416666666666664, "stones_lost_per_game": 11.645833333333334, "passes_per_game": 1.5520833333333333, "games_with_a_pass_before_move_60": 0, "end_two_passes": 92, "end_repetition": 4, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 1.0, "score_as_white": 0.9375, "mean_margin": 64.5, "mean_length": 113.1875, "stones_captured_per_game": 69.0625, "stones_lost_per_game": 3.34375, "passes_per_game": 2.40625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_16/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # Opening line preferences should not make first-line endgame # points artificially unattractive. Flatten them as the board fills. start=cfg.get("endgame_start",12.0) finish=cfg.get("endgame_finish",52.0) for g,threshold in [(489,start),(490,finish)]: W[0][:,g]=0 B[0][g]=-threshold/(finish-start) for p in range(81): W[0][3*p,g]=1/(finish-start) W[0][3*p+1,g]=1/(finish-start) for k in range(1,6): W[k][g,g]=1 for p in range(81): r,c=divmod(p,9) prior=line_prior[min(r,8-r)]+line_prior[min(c,8-c)] delta=cfg.get("endgame_prior",2.0)-prior OW[489,p]+=delta OW[490,p]-=delta'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v15.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(endgame_start=12.0,endgame_finish=52.0,endgame_prior=2.0)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v15.npz",**tensors)print("Saved v15: opening geometry transitions to uniform point value in the endgame.")Saved v15: opening geometry transitions to uniform point value in the endgame.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 17, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.78125, "wins": 75, "losses": 21, "score_as_black": 0.7291666865348816, "score_as_white": 0.8333333134651184, "mean_margin": 48.85416793823242, "mean_length": 117.70833333333333, "stones_captured_per_game": 60.072916666666664, "stones_lost_per_game": 14.34375, "passes_per_game": 1.59375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 3, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.90625, "wins": 29, "losses": 3, "score_as_black": 0.875, "score_as_white": 0.9375, "mean_margin": 64.53125, "mean_length": 112.65625, "stones_captured_per_game": 65.875, "stones_lost_per_game": 3.75, "passes_per_game": 2.0625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 30, "end_repetition": 2, "end_move_limit": 0}}, "files": "/work/pass_17/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v7.py").read()patch=r''' # An endangered stone with several friendly connections is more # likely to represent a substantial group than an isolated stone. for p in range(81): j=u(2,p) W[3][:,j]=0 B[3][j]=-4 W[3][u(0,p),j]=5 for q in adj[p]: W[3][u(0,q),j]+=cfg.get("rescue_size",0.4) for q in [p]+adj[p]: W[3][u(2,q),j]-=10 W[4][u(4,p),u(4,p)]=-12'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v16.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(rescue=8.0,rescue_size=0.4)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v16.npz",**tensors)print("Saved v16: prioritize saving connected groups over harvesting isolated enemy stones.")Saved v16: prioritize saving connected groups over harvesting isolated enemy stones.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 18, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.78125, "wins": 75, "losses": 21, "score_as_black": 0.7916666865348816, "score_as_white": 0.7708333134651184, "mean_margin": 48.33333206176758, "mean_length": 113.16666666666667, "stones_captured_per_game": 55.385416666666664, "stones_lost_per_game": 11.6875, "passes_per_game": 1.5, "games_with_a_pass_before_move_60": 0, "end_two_passes": 92, "end_repetition": 4, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 0.9375, "score_as_white": 1.0, "mean_margin": 66.75, "mean_length": 117.03125, "stones_captured_per_game": 74.8125, "stones_lost_per_game": 3.375, "passes_per_game": 2.40625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_18/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v16.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=json.load(open("/work/policy_config.json"))cfg.update(own_adj=0.8,contact=3.0)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v17.npz",**tensors)print("Saved v17: prefer supported fighting and avoid unsupported multi-stone contact.")Saved v17: prefer supported fighting and avoid unsupported multi-stone contact.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 19, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7708333134651184, "wins": 74, "losses": 22, "score_as_black": 0.75, "score_as_white": 0.7916666865348816, "mean_margin": 49.08333206176758, "mean_length": 115.5, "stones_captured_per_game": 64.15625, "stones_lost_per_game": 13.135416666666666, "passes_per_game": 1.8229166666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 3, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 0.9375, "score_as_white": 1.0, "mean_margin": 68.46875, "mean_length": 115.96875, "stones_captured_per_game": 73.6875, "stones_lost_per_game": 1.96875, "passes_per_game": 2.15625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_19/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v16.py").read()patch=r''' # Pack colour one layer earlier, freeing a channel for short-range # weakness. Capture certainty still uses the longer safety field. for p in range(81): j=u(0,p) W[2][:,j]=0 B[2][j]=0.5 W[2][u(0,p),j]=0.5 W[2][u(1,p),j]=-0.5
j=u(1,p) W[2][:,j]=0 B[2][j]=0 W[2][u(3,p),j]=1
j=u(0,p) W[3][:,j]=0 B[3][j]=0 W[3][u(0,p),j]=1
j=u(1,p) W[3][:,j]=0 B[3][j]=3 W[3][u(0,p),j]=-20 W[3][u(1,p),j]=-1 for q in adj[p]: W[3][u(1,q),j]-=0.5
j=u(2,p) W[3][:,j]=0 size=cfg.get("rescue_size",0.4) B[3][j]=1-0.5*size-20 W[3][u(0,p),j]=20 for q in adj[p]: W[3][u(0,q),j]+=size for q in [p]+adj[p]: W[3][u(2,q),j]-=10
j=u(3,p) W[3][:,j]=0 B[3][j]=1 W[3][u(0,p),j]=-20 for q in [p]+adj[p]: W[3][u(3,q),j]-=1
j=u(5,p) W[3][:,j]=0 B[3][j]=0 for q in adj[p]: W[3][u(5,q),j]+=2 W[3][u(0,q),j]-=2'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v18.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v18.npz",**tensors)print("Saved v18: separate short-range attack pressure from longer-range capture certification.")Saved v18: separate short-range attack pressure from longer-range capture certification.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 20, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7708333134651184, "wins": 74, "losses": 22, "score_as_black": 0.7291666865348816, "score_as_white": 0.8125, "mean_margin": 49.55208206176758, "mean_length": 115.35416666666667, "stones_captured_per_game": 59.197916666666664, "stones_lost_per_game": 10.885416666666666, "passes_per_game": 1.4895833333333333, "games_with_a_pass_before_move_60": 0, "end_two_passes": 96, "end_repetition": 0, "end_move_limit": 0}, "weak": {"games": 32, "score": 0.96875, "wins": 31, "losses": 1, "score_as_black": 0.9375, "score_as_white": 1.0, "mean_margin": 66.96875, "mean_length": 115.53125, "stones_captured_per_game": 74.21875, "stones_lost_per_game": 1.90625, "passes_per_game": 1.9375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_20/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v18.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=json.load(open("/work/policy_config.json"))cfg["temperature_scale"]=4.0cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v19.npz",**tensors)print("Saved v19: sharpen the policy while retaining symmetry among equally valued moves.")Saved v19: sharpen the policy while retaining symmetry among equally valued moves.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 21, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.71875, "wins": 69, "losses": 27, "score_as_black": 0.7291666865348816, "score_as_white": 0.7083333134651184, "mean_margin": 46.125, "mean_length": 115.10416666666667, "stones_captured_per_game": 56.385416666666664, "stones_lost_per_game": 13.177083333333334, "passes_per_game": 1.6875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 3, "end_move_limit": 0}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 71.0625, "mean_length": 118.53125, "stones_captured_per_game": 81.0625, "stones_lost_per_game": 2.59375, "passes_per_game": 2.0625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_21/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v18.py").read()patch=r''' # Smooth influence beyond immediate contact distinguishes contested # boundaries from the interiors of larger frameworks. for p in range(81): for colour,ch in [(0,3),(1,4)]: j=u(ch,p) W[5][:,j]=0 B[5][j]=-cfg["territory_threshold"] for q,dr,dc in rings[p]: weight=float(np.exp(-(dr*dr+dc*dc-1)/ cfg.get("influence_width",3.5))) W[5][u(colour,q),j]+=weight W[5][u(1-colour,q),j]-=1.2*weight'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v20.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(temperature_scale=2.5,influence_width=3.5)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v20.npz",**tensors)print("Saved v20: broader territorial influence discourages redundant interior moves.")Saved v20: broader territorial influence discourages redundant interior moves.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 22, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.78125, "wins": 75, "losses": 21, "score_as_black": 0.75, "score_as_white": 0.8125, "mean_margin": 44.70833206176758, "mean_length": 114.55208333333333, "stones_captured_per_game": 56.916666666666664, "stones_lost_per_game": 13.447916666666666, "passes_per_game": 1.8541666666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 93, "end_repetition": 2, "end_move_limit": 1}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.9375, "score_as_white": 0.9375, "mean_margin": 67.90625, "mean_length": 114.1875, "stones_captured_per_game": 74.9375, "stones_lost_per_game": 2.3125, "passes_per_game": 1.84375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 31, "end_repetition": 1, "end_move_limit": 0}}, "files": "/work/pass_22/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, collections, numpy as npfor k in [14,15,16,18,19,20,21,22]: gs=json.load(open("/work/pass_%d/games.json"%k)) gs=[g for g in gs if g["opponent"]=="strong"] print("pass",k) for col in ["black","white"]: a=[g for g in gs if g["net_color"]==col] first=collections.Counter(next(m["move"] for m in g["moves"] if m["by"]=="opponent") for g in a) margins=[g["margin"] for g in a if g["result"]<0] print(col,"opponent first",first.most_common(6),"loss margins",margins)pass 14black opponent first [(40, 45), (49, 2), (61, 1)] loss margins [-6.5, -20.5, -13.5, -3.5, -9.5, -43.5, -9.5, -22.5, -23.5, -0.5]white opponent first [(40, 45), (48, 3)] loss margins [-0.5, -11.5, -9.5, -4.5, -2.5, -3.5, -14.5, -18.5, -33.5]pass 15black opponent first [(40, 53), (49, 8), (48, 1), (66, 1), (58, 1)] loss margins [-1.5, -11.5, -12.5, -2.5, -7.5, -13.5, -14.5, -8.5, -23.5, -18.5, -7.5, -6.5, -6.5, -18.5, -3.5]white opponent first [(40, 55), (48, 6), (49, 1), (31, 1), (50, 1)] loss margins [-10.5, -18.5, -4.5, -4.5, -5.5, -8.5, -5.5, -0.5, -18.5, -16.5, -6.5, -3.5, -5.5, -2.5, -0.5, -9.5]pass 16black opponent first [(40, 41), (49, 4), (58, 1), (50, 1), (11, 1)] loss margins [-23.5, -21.5, -8.5, -2.5, -16.5, 1.5, -9.5, -10.5, -28.5, -3.5, -1.5]white opponent first [(40, 45), (48, 2), (31, 1)] loss margins [-7.5, -8.5, -4.5, -32.5, -9.5, -21.5, 39.5, -3.5]pass 18black opponent first [(40, 42), (49, 5), (77, 1)] loss margins [-19.5, -9.5, -15.5, -0.5, -15.5, -17.5, -1.5, -11.5, -2.5, -15.5]white opponent first [(40, 42), (48, 4), (31, 2)] loss margins [-9.5, -7.5, -12.5, -17.5, -3.5, -8.5, -0.5, -4.5, -0.5, -5.5, -3.5]pass 19black opponent first [(40, 42), (49, 6)] loss margins [-1.5, -20.5, -5.5, -5.5, -8.5, -15.5, 11.5, -9.5, -4.5, -13.5, -9.5, -17.5]white opponent first [(40, 42), (48, 4), (31, 2)] loss margins [-4.5, -11.5, -9.5, -4.5, -0.5, -15.5, -17.5, -9.5, -4.5, -4.5]pass 20black opponent first [(40, 44), (49, 2), (58, 1), (43, 1)] loss margins [-10.5, -13.5, -7.5, -24.5, -19.5, -6.5, -31.5, -4.5, -12.5, -17.5, -8.5, -0.5, -4.5]white opponent first [(40, 42), (48, 5), (51, 1)] loss margins [-10.5, -5.5, -0.5, -8.5, -2.5, -43.5, -2.5, -11.5, -9.5]pass 21black opponent first [(40, 39), (49, 5), (58, 2), (48, 2)] loss margins [-17.5, 36.5, -24.5, -1.5, -22.5, -16.5, -2.5, -3.5, -5.5, -8.5, -15.5, -21.5, -0.5]white opponent first [(40, 46), (48, 2)] loss margins [-28.5, -11.5, -14.5, -5.5, -9.5, -3.5, -9.5, -2.5, -9.5, -4.5, -17.5, -13.5, -22.5, -4.5]pass 22black opponent first [(40, 45), (49, 3)] loss margins [22.5, -5.5, -4.5, -7.5, -11.5, -3.5, -19.5, -8.5, -3.5, -14.5, -6.5, -22.5]white opponent first [(40, 46), (50, 1), (48, 1)] loss margins [-64.5, -7.5, -10.5, -7.5, -12.5, -9.5, -5.5, -5.5, -7.5]import json, numpy as npsource=open("/work/policy_design_v18.py").read()patch=r''' # Four-four openings balance central influence with access to a # corner, without committing to direct contact at the centre. W[0][:,489]=0 B[0][489]=1 for p in range(81): W[0][3*p,489]=-1 W[0][3*p+1,489]=-1 for k in range(1,6): W[k][489,489]=1 for p in [30,32,48,50]: OW[489,p]+=cfg.get("opening_four_four",6.0)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v21.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(opening_four_four=6.0,temperature_scale=2.5)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v21.npz",**tensors)print("Saved v21: symmetric four-four opening choices.")Saved v21: symmetric four-four opening choices.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 23, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.75, "wins": 72, "losses": 24, "score_as_black": 0.7083333134651184, "score_as_white": 0.7916666865348816, "mean_margin": 47.69791793823242, "mean_length": 115.57291666666667, "stones_captured_per_game": 64.64583333333333, "stones_lost_per_game": 12.427083333333334, "passes_per_game": 1.8125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 94, "end_repetition": 2, "end_move_limit": 0}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 71.15625, "mean_length": 115.65625, "stones_captured_per_game": 77.59375, "stones_lost_per_game": 2.75, "passes_per_game": 1.8125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_23/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v18.py").read()patch=r''' # Value additional territory at the contested boundary more than # safe consolidation inside an existing sphere of influence. for p in range(81): B[5][u(3,p)]=-cfg.get("own_territory_threshold",0.5)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v22.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(own_territory=3.0,own_territory_threshold=0.5)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v22.npz",**tensors)print("Saved v22: greater territorial ambition at contested boundaries.")Saved v22: greater territorial ambition at contested boundaries.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 24, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.84375, "wins": 81, "losses": 15, "score_as_black": 0.8333333134651184, "score_as_white": 0.8541666865348816, "mean_margin": 52.375, "mean_length": 116.34375, "stones_captured_per_game": 62.114583333333336, "stones_lost_per_game": 10.927083333333334, "passes_per_game": 1.9895833333333333, "games_with_a_pass_before_move_60": 0, "end_two_passes": 91, "end_repetition": 3, "end_move_limit": 2}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 69.65625, "mean_length": 117.0625, "stones_captured_per_game": 74.875, "stones_lost_per_game": 3.28125, "passes_per_game": 1.75, "games_with_a_pass_before_move_60": 0, "end_two_passes": 30, "end_repetition": 1, "end_move_limit": 1}}, "files": "/work/pass_24/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 25, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.78125, "wins": 100, "losses": 28, "score_as_black": 0.796875, "score_as_white": 0.765625, "mean_margin": 46.8359375, "mean_length": 117.5234375, "stones_captured_per_game": 59.65625, "stones_lost_per_game": 15.7265625, "passes_per_game": 1.8203125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 122, "end_repetition": 5, "end_move_limit": 1}}, "files": "/work/pass_25/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v22.py").read()patch=r''' # A third-line framework is economical on 9x9: it can limit a # central group while retaining room for eyes on the outside. for p in range(81): r,c=divmod(p,9) if max(abs(r-4),abs(c-4))==2: OB[p]+=cfg.get("framework_bonus",2.0)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v23.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg["framework_bonus"]=2.0ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v23.npz",**tensors)print("Saved v23: a symmetric third-line framework prior.")Saved v23: a symmetric third-line framework prior.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 26, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.84375, "wins": 81, "losses": 15, "score_as_black": 0.8333333134651184, "score_as_white": 0.8541666865348816, "mean_margin": 55.76041793823242, "mean_length": 117.5, "stones_captured_per_game": 64.08333333333333, "stones_lost_per_game": 10.739583333333334, "passes_per_game": 1.5416666666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 96, "end_repetition": 0, "end_move_limit": 0}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 70.5, "mean_length": 116.84375, "stones_captured_per_game": 71.28125, "stones_lost_per_game": 2.78125, "passes_per_game": 1.5, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_26/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 27, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.796875, "wins": 102, "losses": 26, "score_as_black": 0.75, "score_as_white": 0.84375, "mean_margin": 53.0703125, "mean_length": 115.65625, "stones_captured_per_game": 60.9921875, "stones_lost_per_game": 9.9609375, "passes_per_game": 1.6015625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 119, "end_repetition": 8, "end_move_limit": 1}}, "files": "/work/pass_27/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as np, collectionsg=json.load(open("/work/pass_27/games.json"))b=np.load("/work/pass_27/boards.npy")N=[]for p in range(81): r,c=divmod(p,9) N.append([rr*9+cc for rr,cc in [(r-1,c),(r+1,c),(r,c-1),(r,c+1)] if 0<=rr<9 and 0<=cc<9])def groups(board): out=[]; seen=set() for p in range(81): if board[p]==0 or p in seen: continue st={p}; lib=set(); seen.add(p); todo=[p] while todo: q=todo.pop() for a in N[q]: if board[a]==0: lib.add(a) elif board[a]==board[p] and a not in seen: st.add(a);seen.add(a);todo.append(a) out.append((int(board[p]),st,lib)) return outfor i,x in enumerate(g): if x["end"]!="repetition": continue t=len(x["moves"])-1 eq=[j for j in range(t) if np.array_equal(b[i,j],b[i,t])] print("\nREPEAT",i,x["net_color"],"result",x["result"],"margin",x["margin"], "ply",t,"earlier",eq) print(json.dumps(x["moves"][-8:])) pre=b[i,t-1] mover=1 if t%2==0 else -1 p=x["moves"][-1]["move"] print("move",divmod(p,9),"adjacent",[int(pre[q]) for q in N[p]], "capturable",[(len(s),sorted(s),list(l)) for co,s,l in groups(pre) if co==-mover and l=={p}]) print("\n".join("".join({0:".",1:"X",-1:"O"}[int(z)] for z in row) for row in pre.reshape(9,9)))
REPEAT 65 white result -1 margin 32.5 ply 81 earlier [78][{"ply": 74, "by": "opponent", "move": 7}, {"ply": 75, "by": "net", "move": 0, "top5": [[0, 1.0], [5, 0.0], [8, 0.0], [79, 0.0], [4, 0.0]]}, {"ply": 76, "by": "opponent", "move": 79}, {"ply": 77, "by": "net", "move": 5, "top5": [[5, 0.6106], [8, 0.3893], [4, 0.0001], [81, 0.0], [6, 0.0]]}, {"ply": 78, "by": "opponent", "move": 6}, {"ply": 79, "by": "net", "move": 8, "top5": [[8, 1.0], [81, 0.0], [26, 0.0], [30, 0.0], [65, 0.0]]}, {"ply": 80, "by": "opponent", "move": 26}, {"ply": 81, "by": "net", "move": 17, "top5": [[17, 0.8699], [81, 0.1301], [8, 0.0], [30, 0.0], [65, 0.0]]}]move (1, 8) adjacent [0, 1, 1] capturable [(1, [26], [17])]OOXX.OXX..OXXXXXX.OOXOOOXOXO.O.O.OOO.OOO.OO.OO.O.OOOOOOOOOXXOXX.O.OXXXXXO.OX.X.X.
REPEAT 73 white result 1 margin 15.5 ply 56 earlier [53][{"ply": 49, "by": "net", "move": 16, "top5": [[16, 0.8155], [68, 0.0669], [6, 0.0591], [27, 0.0358], [60, 0.0153]]}, {"ply": 50, "by": "opponent", "move": 69}, {"ply": 51, "by": "net", "move": 6, "top5": [[6, 1.0], [60, 0.0], [68, 0.0], [27, 0.0], [18, 0.0]]}, {"ply": 52, "by": "opponent", "move": 17}, {"ply": 53, "by": "net", "move": 60, "top5": [[60, 0.9975], [68, 0.0019], [27, 0.0003], [26, 0.0002], [18, 0.0]]}, {"ply": 54, "by": "opponent", "move": 54}, {"ply": 55, "by": "net", "move": 72, "top5": [[72, 1.0], [68, 0.0], [27, 0.0], [26, 0.0], [18, 0.0]]}, {"ply": 56, "by": "opponent", "move": 63}]move (7, 0) adjacent [0, -1, -1] capturable [(1, [72], [63])]...X..O...XOXXO.OX.XOOOOO...XXXO.OO.OOOXXOO..O.OXXO....OOOXOO...OXXX.X..OX.......
REPEAT 83 white result -1 margin 5.5 ply 107 earlier [102, 103][{"ply": 100, "by": "opponent", "move": 72}, {"ply": 101, "by": "net", "move": 4, "top5": [[4, 1.0], [2, 0.0], [81, 0.0], [1, 0.0], [0, 0.0]]}, {"ply": 102, "by": "opponent", "move": 2}, {"ply": 103, "by": "net", "move": 81, "top5": [[81, 0.6457], [5, 0.3543], [32, 0.0], [52, 0.0], [44, 0.0]]}, {"ply": 104, "by": "opponent", "move": 5}, {"ply": 105, "by": "net", "move": 4, "top5": [[4, 0.9964], [81, 0.0036], [3, 0.0], [32, 0.0], [15, 0.0]]}, {"ply": 106, "by": "opponent", "move": 81}, {"ply": 107, "by": "net", "move": 3, "top5": [[3, 1.0], [81, 0.0], [5, 0.0], [32, 0.0], [0, 0.0]]}]move (0, 3) adjacent [1, 1, -1] capturable []X.X.O.OO.XXXXXO.OOXXXOOOOO.XXOOO.O.OXXOOXOOO.XXOXXXO.OXXXXXXOOOXXXO.XXXOXXXXXXXXO
REPEAT 86 white result 1 margin 44.5 ply 102 earlier [99][{"ply": 95, "by": "net", "move": 7, "top5": [[7, 0.9804], [78, 0.0196], [81, 0.0], [16, 0.0], [26, 0.0]]}, {"ply": 96, "by": "opponent", "move": 35}, {"ply": 97, "by": "net", "move": 16, "top5": [[16, 1.0], [78, 0.0], [8, 0.0], [81, 0.0], [36, 0.0]]}, {"ply": 98, "by": "opponent", "move": 5}, {"ply": 99, "by": "net", "move": 6, "top5": [[6, 1.0], [15, 0.0], [8, 0.0], [78, 0.0], [81, 0.0]]}, {"ply": 100, "by": "opponent", "move": 8}, {"ply": 101, "by": "net", "move": 26, "top5": [[26, 1.0], [78, 0.0], [81, 0.0], [36, 0.0], [80, 0.0]]}, {"ply": 102, "by": "opponent", "move": 17}]move (1, 8) adjacent [0, -1, -1] capturable [(1, [26], [17])]O.OOO.OO..OOO.O.O.OO.OOOOXOOOOXXXXXX.XOXXOOXXXOOXOOOX.OOOOOO.OX.O.OO.OOOO.OO.O.X.
REPEAT 91 white result -1 margin 22.5 ply 95 earlier [92][{"ply": 88, "by": "opponent", "move": 8}, {"ply": 89, "by": "net", "move": 26, "top5": [[26, 0.7455], [3, 0.1838], [73, 0.0693], [72, 0.0008], [81, 0.0005]]}, {"ply": 90, "by": "opponent", "move": 17}, {"ply": 91, "by": "net", "move": 3, "top5": [[3, 0.7219], [73, 0.2723], [72, 0.003], [81, 0.0027], [74, 0.0002]]}, {"ply": 92, "by": "opponent", "move": 63}, {"ply": 93, "by": "net", "move": 4, "top5": [[4, 1.0], [73, 0.0], [81, 0.0], [74, 0.0], [72, 0.0]]}, {"ply": 94, "by": "opponent", "move": 2}, {"ply": 95, "by": "net", "move": 3, "top5": [[3, 0.696], [73, 0.2975], [81, 0.0033], [74, 0.0031], [4, 0.0]]}]move (0, 3) adjacent [1, 1, 0] capturable [(1, [2], [3])]OOX..X.OX.OOXXXXXXOOOXOOOX.O.OXXXOXXOOOOXOOOOOXOXOO.OOXXXXXOOO.XXXXO.O.O...XOOOOO
REPEAT 108 white result -1 margin 37.5 ply 93 earlier [90][{"ply": 86, "by": "opponent", "move": 44}, {"ply": 87, "by": "net", "move": 34, "top5": [[34, 0.9892], [3, 0.0073], [53, 0.0035], [1, 0.0], [10, 0.0]]}, {"ply": 88, "by": "opponent", "move": 77}, {"ply": 89, "by": "net", "move": 53, "top5": [[53, 1.0], [3, 0.0], [1, 0.0], [81, 0.0], [10, 0.0]]}, {"ply": 90, "by": "opponent", "move": 0}, {"ply": 91, "by": "net", "move": 3, "top5": [[3, 1.0], [10, 0.0], [81, 0.0], [1, 0.0], [5, 0.0]]}, {"ply": 92, "by": "opponent", "move": 5}, {"ply": 93, "by": "net", "move": 4, "top5": [[15, 0.7621], [4, 0.2353], [10, 0.0019], [81, 0.0007], [3, 0.0]]}]move (0, 4) adjacent [1, 0, 1] capturable [(1, [5], [4])]X.X..XOX.X.XXXO.OXXXXOOOOOXXOO.O.OOXO.OO.O.OXOO.OO.OOOO.OO.OO.O.OOOOXOOXO.OOXXXX.
REPEAT 119 white result 1 margin 36.5 ply 96 earlier [90][{"ply": 89, "by": "net", "move": 50, "top5": [[50, 1.0], [81, 0.0], [42, 0.0], [41, 0.0], [23, 0.0]]}, {"ply": 90, "by": "opponent", "move": 36}, {"ply": 91, "by": "net", "move": 27, "top5": [[18, 0.5245], [27, 0.4746], [81, 0.0009], [42, 0.0], [41, 0.0]]}, {"ply": 92, "by": "opponent", "move": 0}, {"ply": 93, "by": "net", "move": 18, "top5": [[18, 1.0], [0, 0.0], [1, 0.0], [2, 0.0], [3, 0.0]]}, {"ply": 94, "by": "opponent", "move": 1}, {"ply": 95, "by": "net", "move": 81, "top5": [[81, 0.9901], [0, 0.0097], [9, 0.0002], [42, 0.0], [41, 0.0]]}, {"ply": 96, "by": "opponent", "move": 9}]move (1, 0) adjacent [0, -1, -1] capturable [(2, [18, 27], [9])].XO.OX.XO.OOO.OXOOOXO.O.OO.OXOO.OO.OXXO.O..OOXXOOOOO.OXXOOO.OOOXXXXOOO.O.X.XXO.OO
REPEAT 123 white result -1 margin 37.5 ply 105 earlier [102][{"ply": 98, "by": "opponent", "move": 4}, {"ply": 99, "by": "net", "move": 16, "top5": [[16, 1.0], [8, 0.0], [62, 0.0], [81, 0.0], [63, 0.0]]}, {"ply": 100, "by": "opponent", "move": 62}, {"ply": 101, "by": "net", "move": 53, "top5": [[53, 0.7493], [44, 0.2494], [81, 0.0013], [70, 0.0], [63, 0.0]]}, {"ply": 102, "by": "opponent", "move": 8}, {"ply": 103, "by": "net", "move": 44, "top5": [[44, 1.0], [81, 0.0], [17, 0.0], [16, 0.0], [62, 0.0]]}, {"ply": 104, "by": "opponent", "move": 62}, {"ply": 105, "by": "net", "move": 53, "top5": [[53, 0.9987], [81, 0.0013], [44, 0.0], [17, 0.0], [16, 0.0]]}]move (5, 8) adjacent [0, 1, 1] capturable [(1, [62], [53])]XXXXXXXXXXOOXX.X..OOOOOXXXXOO.OOXOXX.OO.OOOX.OOOO.OOX.O.OOOOOOX.O.OO.O.OO.OO.OOOOimport json, numpy as npsource=open("/work/policy_design_v23.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=json.load(open("/work/policy_config.json"))cfg["repeat_risk"]=80.0cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v24.npz",**tensors)print("Saved v24: isolated singleton recapture risk now dominates all local tactical bonuses.")print("This avoids treating several nearby weak stones as justification for a repetition-prone recapture.")Saved v24: isolated singleton recapture risk now dominates all local tactical bonuses.This avoids treating several nearby weak stones as justification for a repetition-prone recapture.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 28, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.8541666865348816, "wins": 82, "losses": 14, "score_as_black": 0.8333333134651184, "score_as_white": 0.875, "mean_margin": 53.98958206176758, "mean_length": 114.01041666666667, "stones_captured_per_game": 62.083333333333336, "stones_lost_per_game": 7.666666666666667, "passes_per_game": 1.3854166666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 92, "end_repetition": 4, "end_move_limit": 0}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 72.71875, "mean_length": 118.5625, "stones_captured_per_game": 70.1875, "stones_lost_per_game": 1.25, "passes_per_game": 1.65625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_28/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, collectionsg=json.load(open("/work/pass_28/games.json"))print(collections.Counter((x["opponent"],x["end"],x["result"]) for x in g))for i,x in enumerate(g): if x["end"]=="repetition": print(i,x["net_color"],x["result"],x["margin"],x["moves"][-4:])Counter({('strong', 'two passes', 1): 80, ('weak', 'two passes', 1): 32, ('strong', 'two passes', -1): 12, ('strong', 'repetition', 1): 2, ('strong', 'repetition', -1): 2})26 black 1 53.5 [{'ply': 104, 'by': 'net', 'move': 67, 'top5': [[67, 1.0], [77, 0.0], [72, 0.0], [81, 0.0], [55, 0.0]]}, {'ply': 105, 'by': 'opponent', 'move': 77}, {'ply': 106, 'by': 'net', 'move': 75, 'top5': [[75, 1.0], [72, 0.0], [69, 0.0], [81, 0.0], [0, 0.0]]}, {'ply': 107, 'by': 'opponent', 'move': 76}]44 black -1 -5.5 [{'ply': 87, 'by': 'opponent', 'move': 81}, {'ply': 88, 'by': 'net', 'move': 0, 'top5': [[0, 1.0], [5, 0.0], [81, 0.0], [6, 0.0], [75, 0.0]]}, {'ply': 89, 'by': 'opponent', 'move': 27}, {'ply': 90, 'by': 'net', 'move': 9, 'top5': [[9, 1.0], [5, 0.0], [81, 0.0], [0, 0.0], [6, 0.0]]}]55 white 1 20.5 [{'ply': 69, 'by': 'net', 'move': 62, 'top5': [[26, 0.4909], [54, 0.2762], [62, 0.1655], [1, 0.0326], [9, 0.0241]]}, {'ply': 70, 'by': 'opponent', 'move': 54}, {'ply': 71, 'by': 'net', 'move': 72, 'top5': [[72, 1.0], [26, 0.0], [1, 0.0], [71, 0.0], [9, 0.0]]}, {'ply': 72, 'by': 'opponent', 'move': 63}]84 white -1 -6.5 [{'ply': 108, 'by': 'opponent', 'move': 81}, {'ply': 109, 'by': 'net', 'move': 6, 'top5': [[6, 1.0], [81, 0.0], [46, 0.0], [60, 0.0], [8, 0.0]]}, {'ply': 110, 'by': 'opponent', 'move': 8}, {'ply': 111, 'by': 'net', 'move': 7, 'top5': [[7, 0.9999], [16, 0.0001], [81, 0.0], [46, 0.0], [17, 0.0]]}]import json, numpy as npg=json.load(open("/work/pass_28/games.json"))b=np.load("/work/pass_28/boards.npy")for i in [44,84]: x=g[i]; t=len(x["moves"])-1 print("\nGAME",i,"colour",x["net_color"],"last moves",x["moves"][-10:]) for j in range(t-4,t+1): print("ply",j,"action",x["moves"][j]["move"]) print("\n".join("".join({0:".",1:"X",-1:"O"}[int(z)] for z in row) for row in b[i,j].reshape(9,9)))
GAME 44 colour black last moves [{'ply': 81, 'by': 'opponent', 'move': 63}, {'ply': 82, 'by': 'net', 'move': 54, 'top5': [[54, 0.9406], [9, 0.0594], [7, 0.0], [81, 0.0], [0, 0.0]]}, {'ply': 83, 'by': 'opponent', 'move': 35}, {'ply': 84, 'by': 'net', 'move': 9, 'top5': [[9, 1.0], [7, 0.0], [81, 0.0], [0, 0.0], [8, 0.0]]}, {'ply': 85, 'by': 'opponent', 'move': 7}, {'ply': 86, 'by': 'net', 'move': 18, 'top5': [[18, 1.0], [0, 0.0], [1, 0.0], [2, 0.0], [3, 0.0]]}, {'ply': 87, 'by': 'opponent', 'move': 81}, {'ply': 88, 'by': 'net', 'move': 0, 'top5': [[0, 1.0], [5, 0.0], [81, 0.0], [6, 0.0], [75, 0.0]]}, {'ply': 89, 'by': 'opponent', 'move': 27}, {'ply': 90, 'by': 'net', 'move': 9, 'top5': [[9, 1.0], [5, 0.0], [81, 0.0], [0, 0.0], [6, 0.0]]}]ply 86 action 18.OOOO..O.XOOOOOOOXXOXXXXO.O.XXOOOOOOXXXXOXOXOX.XOOXXXXXX.XOXOX.OXXXOOOXX.OX.XXX.Xply 87 action 81.OOOO..O.XOOOOOOOXXOXXXXO.O.XXOOOOOOXXXXOXOXOX.XOOXXXXXX.XOXOX.OXXXOOOXX.OX.XXX.Xply 88 action 0XOOOO..O.XOOOOOOOXXOXXXXO.O.XXOOOOOOXXXXOXOXOX.XOOXXXXXX.XOXOX.OXXXOOOXX.OX.XXX.Xply 89 action 27.OOOO..O..OOOOOOOX.OXXXXO.OOXXOOOOOOXXXXOXOXOX.XOOXXXXXX.XOXOX.OXXXOOOXX.OX.XXX.Xply 90 action 9.OOOO..O.XOOOOOOOX.OXXXXO.OOXXOOOOOOXXXXOXOXOX.XOOXXXXXX.XOXOX.OXXXOOOXX.OX.XXX.X
GAME 84 colour white last moves [{'ply': 102, 'by': 'opponent', 'move': 8}, {'ply': 103, 'by': 'net', 'move': 7, 'top5': [[7, 0.9999], [16, 0.0001], [81, 0.0], [46, 0.0], [17, 0.0]]}, {'ply': 104, 'by': 'opponent', 'move': 0}, {'ply': 105, 'by': 'net', 'move': 16, 'top5': [[16, 1.0], [81, 0.0], [6, 0.0], [46, 0.0], [60, 0.0]]}, {'ply': 106, 'by': 'opponent', 'move': 81}, {'ply': 107, 'by': 'net', 'move': 17, 'top5': [[17, 1.0], [81, 0.0], [6, 0.0], [46, 0.0], [60, 0.0]]}, {'ply': 108, 'by': 'opponent', 'move': 81}, {'ply': 109, 'by': 'net', 'move': 6, 'top5': [[6, 1.0], [81, 0.0], [46, 0.0], [60, 0.0], [8, 0.0]]}, {'ply': 110, 'by': 'opponent', 'move': 8}, {'ply': 111, 'by': 'net', 'move': 7, 'top5': [[7, 0.9999], [16, 0.0001], [81, 0.0], [46, 0.0], [17, 0.0]]}]ply 107 action 17XXXXXX.O.XXXX.XXOOXOXXX.XXXOOOXXXXXX.OOOXOXOOO.OXXOOO.XOOOXO.OOXXXXXXO.OXXXXXXOOOply 108 action 81XXXXXX.O.XXXX.XXOOXOXXX.XXXOOOXXXXXX.OOOXOXOOO.OXXOOO.XOOOXO.OOXXXXXXO.OXXXXXXOOOply 109 action 6XXXXXXOO.XXXX.XXOOXOXXX.XXXOOOXXXXXX.OOOXOXOOO.OXXOOO.XOOOXO.OOXXXXXXO.OXXXXXXOOOply 110 action 8XXXXXX..XXXXX.XX..XOXXX.XXXOOOXXXXXX.OOOXOXOOO.OXXOOO.XOOOXO.OOXXXXXXO.OXXXXXXOOOply 111 action 7XXXXXX.OXXXXX.XX..XOXXX.XXXOOOXXXXXX.OOOXOXOOO.OXXOOO.XOOOXO.OOXXXXXXO.OXXXXXXOOOimport json, numpy as npsource=open("/work/policy_design_v23.py").read()patch=r''' # Compress the two material counts into one total, leaving 24 # auxiliary units for stronger edge self-atari certification. for k in range(6): W[k][:,486:]=0 B[k][486:]=0 if k: W[k][486:,:]=0 OW[486:,:]=0 for p in range(81): W[0][3*p,486]=1 W[0][3*p+1,486]=1 W[0][2,487]=1 for k in range(1,6): W[k][486,486]=1 W[k][487,487]=1 OW[486,81]=cfg["pass_per_stone"] OW[487,81]=cfg["pass_white"]
edge_points=[] for p in range(81): r,c=divmod(p,9) if r in [0,8] and c in [0,1,2,4,6,7,8]: edge_points.append(p) elif c in [0,8] and r in [1,2,4,6,7]: edge_points.append(p) assert len(edge_points)==24 for i,p in enumerate(edge_points): g=488+i # Exact singleton captures, rather than approximate group danger. for q in adj[p]: W[3][u(5,q),g]+=1
# Multiple threatened contacts can justify a larger capture. B[4][g]=-1 for q in adj[p]: W[4][u(3,q),g]+=1
j=u(3,p) W[4][:,j]=0 B[4][j]=0 W[4][u(4,p),j]=1 W[4][g,j]=-2
W[5][u(3,p),g]=1 W[5][g,g]=-2 W[5][u(3,p),u(0,p)]=0 OW[g,p]=-cfg.get("edge_self_atari",32.0)'''source=source.replace(' OW *= cfg["temperature_scale"]', patch+'\n OW *= cfg["temperature_scale"]')open("/work/policy_design_v25.py","w").write(source)open("/work/policy_design.py","w").write(source)cfg=json.load(open("/work/policy_config.json"))cfg.update(repeat_risk=80.0,edge_self_atari=32.0)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v25.npz",**tensors)print("Saved v25: exact small-capture evidence guards against false edge captures and self-atari cycles.")Saved v25: exact small-capture evidence guards against false edge captures and self-atari cycles.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 29, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.7604166865348816, "wins": 73, "losses": 23, "score_as_black": 0.6875, "score_as_white": 0.8333333134651184, "mean_margin": 45.82291793823242, "mean_length": 116.82291666666667, "stones_captured_per_game": 58.46875, "stones_lost_per_game": 13.416666666666666, "passes_per_game": 1.9791666666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 94, "end_repetition": 1, "end_move_limit": 1}, "weak": {"games": 32, "score": 0.9375, "wins": 30, "losses": 2, "score_as_black": 0.875, "score_as_white": 1.0, "mean_margin": 64.21875, "mean_length": 117.0, "stones_captured_per_game": 72.96875, "stones_lost_per_game": 2.78125, "passes_per_game": 2.46875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_29/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v23.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=dict( capture=9.0,rescue=8.0,attack=1.4,self_atari=7.5, eye=11.0,connection=0.9,contact=3.0, own_territory=3.0,own_territory_threshold=0.5, enemy_territory=1.0,territory_threshold=1.5, pass_base=-12.0,pass_per_stone=0.10,pass_white=3.35, own_adj=0.8,own_diag=0.5,own_two=0.7,own_knight=0.3, enemy_adj=1.3,enemy_diag=0.7,enemy_two=0.4, temperature_scale=2.5,surrounded_penalty=10.0, repeat_risk=80.0,rescue_size=0.4,framework_bonus=4.0,)cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)np.savez("/work/policy_v26.npz",**tensors)print("Saved v26: restore capture responsiveness and reinforce the third-line framework.")Saved v26: restore capture responsiveness and reinforce the third-line framework.{"games":{"strong":96,"weak":32}}{"ok": true, "pass": 30, "kind": "games", "per_opponent": {"strong": {"games": 96, "score": 0.8125, "wins": 78, "losses": 18, "score_as_black": 0.8333333134651184, "score_as_white": 0.7916666865348816, "mean_margin": 48.76041793823242, "mean_length": 116.85416666666667, "stones_captured_per_game": 60.927083333333336, "stones_lost_per_game": 12.3125, "passes_per_game": 1.8541666666666667, "games_with_a_pass_before_move_60": 0, "end_two_passes": 91, "end_repetition": 4, "end_move_limit": 1}, "weak": {"games": 32, "score": 1.0, "wins": 32, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 71.875, "mean_length": 119.71875, "stones_captured_per_game": 71.875, "stones_lost_per_game": 1.84375, "passes_per_game": 1.46875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 32, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_30/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, numpy as npsource=open("/work/policy_design_v23.py").read()open("/work/policy_design.py","w").write(source)ns={}exec(compile(source,"/work/policy_design.py","exec"),ns)cfg=json.load(open("/work/policy_config.json"))cfg["framework_bonus"]=2.0cfg,tensors=ns["build"](save,cfg)json.dump(cfg,open("/work/policy_config.json","w"),indent=2)json.dump(cfg,open("/work/candidate_A_config.json","w"),indent=2)np.savez("/work/candidate_A.npz",**tensors)print("Restored candidate A for independent validation.")print("Parameters:",sum(a.size for a in tensors.values()))print("Finite tensors:",all(np.isfinite(a).all() for a in tensors.values()))Restored candidate A for independent validation.Parameters: 1480274Finite tensors: True{"games":{"strong":128}}{"ok": true, "pass": 31, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.84375, "wins": 108, "losses": 20, "score_as_black": 0.78125, "score_as_white": 0.90625, "mean_margin": 55.4296875, "mean_length": 117.1875, "stones_captured_per_game": 65.8046875, "stones_lost_per_game": 10.2109375, "passes_per_game": 1.609375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 124, "end_repetition": 4, "end_move_limit": 0}}, "files": "/work/pass_31/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 32, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.84375, "wins": 108, "losses": 20, "score_as_black": 0.796875, "score_as_white": 0.890625, "mean_margin": 52.609375, "mean_length": 118.875, "stones_captured_per_game": 63.484375, "stones_lost_per_game": 10.703125, "passes_per_game": 2.09375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 127, "end_repetition": 0, "end_move_limit": 1}}, "files": "/work/pass_32/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 33, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8359375, "wins": 107, "losses": 21, "score_as_black": 0.8125, "score_as_white": 0.859375, "mean_margin": 52.765625, "mean_length": 115.8203125, "stones_captured_per_game": 60.625, "stones_lost_per_game": 9.171875, "passes_per_game": 1.640625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 126, "end_repetition": 2, "end_move_limit": 0}}, "files": "/work/pass_33/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 34, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8359375, "wins": 107, "losses": 21, "score_as_black": 0.796875, "score_as_white": 0.875, "mean_margin": 55.453125, "mean_length": 118.625, "stones_captured_per_game": 66.2109375, "stones_lost_per_game": 9.4375, "passes_per_game": 1.6953125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 2, "end_move_limit": 1}}, "files": "/work/pass_34/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"weak":64,"random":64}}{"ok": true, "pass": 35, "kind": "games", "per_opponent": {"random": {"games": 64, "score": 1.0, "wins": 64, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 65.1875, "mean_length": 109.25, "stones_captured_per_game": 63.625, "stones_lost_per_game": 0.34375, "passes_per_game": 1.109375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 63, "end_repetition": 1, "end_move_limit": 0}, "weak": {"games": 64, "score": 1.0, "wins": 64, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 70.015625, "mean_length": 117.359375, "stones_captured_per_game": 69.671875, "stones_lost_per_game": 2.078125, "passes_per_game": 1.640625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 62, "end_repetition": 2, "end_move_limit": 0}}, "files": "/work/pass_35/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"positions":[{"board":[".........",".........",".........","....X....","...XOX...",".........",".........",".........","........."],"to_move":"black"},{"board":[".........",".........",".........","....O....","...OXO...",".........",".........",".........","........."],"to_move":"black"},{"board":[".........",".........","...XX....","..XOOX...","..XO.....","...X.....",".........",".........","........."],"to_move":"black"},{"board":[".........",".........","...OO....","..OXXO...","..OX.....","...O.....",".........",".........","........."],"to_move":"black"},{"board":["XXXXXXXXX","XXXXXXXXX","XXXXXXXXX","XXXXXXXXX","XXXX.X.XX","XXXXXXXXX","XXXXXXXXX","XXXXXXXXX","XXXXXXXXX"],"to_move":"black"},{"board":[".........",".........",".........","......OX.",".......OX","......OX.",".........",".........","........."],"to_move":"black"},{"board":[".........",".........",".........","......OXX",".......OO","......OXX",".........",".........","........."],"to_move":"black"},{"board":[".........",".........",".........","...OXO...","...X.X...","...OXO...",".........",".........","........."],"to_move":"black"},{"board":[".........",".........",".........",".........",".........",".........",".........",".........","........."],"to_move":"black"}],"activations":true}{"ok": true, "pass": 36, "kind": "positions", "positions": 9, "top3_per_position": [[["(5,4)", 1.0], ["(4,2)", 0.0], ["(4,6)", 0.0]], [["(5,4)", 1.0], ["(2,4)", 0.0], ["(4,2)", 0.0]], [["(4,4)", 1.0], ["(2,2)", 0.0], ["(5,2)", 0.0]], [["(4,4)", 1.0], ["(3,6)", 0.0], ["(6,3)", 0.0]], [["pass", 1.0], ["(4,6)", 0.0], ["(4,4)", 0.0]], [["(2,6)", 0.4972], ["(6,6)", 0.4972], ["(3,5)", 0.0012]], [["(4,6)", 1.0], ["(2,6)", 0.0], ["(6,6)", 0.0]], [["(2,3)", 0.1248], ["(3,2)", 0.1248], ["(2,5)", 0.1248]], [["(2,6)", 0.0977], ["(2,2)", 0.0977], ["(6,6)", 0.0977]]], "files": "/work/pass_36/", "readme": "inputs.npy (P, 243) the network inputs; legal.npy (P, 82) the legal moves (for 'board' positions: the empty points and pass; suicide and ko are not checked); logits.npy, probs.npy (P, 82); act_L1.npy .. act_L6.npy (P, 512) the hidden activations after ReLU."}{"positions":[{"board":[".........",".........",".........","....O....","...OXO...",".........",".........",".........","........."],"to_move":"white"},{"board":[".........",".........",".........","....X....","...XOX...",".........",".........",".........","........."],"to_move":"white"},{"board":[".........",".........","...OO....","..OXXO...","..OX.....","...O.....",".........",".........","........."],"to_move":"white"},{"board":[".........",".........","...XX....","..XOOX...","..XO.....","...X.....",".........",".........","........."],"to_move":"white"},{"board":["OOOOOOOOO","OOOOOOOOO","OOOOOOOOO","OOOOOOOOO","OOOO.O.OO","OOOOOOOOO","OOOOOOOOO","OOOOOOOOO","OOOOOOOOO"],"to_move":"white"},{"board":[".........",".........",".........","......XO.",".......XO","......XO.",".........",".........","........."],"to_move":"white"},{"board":[".........",".........",".........","......XOO",".......XX","......XOO",".........",".........","........."],"to_move":"white"},{"board":[".........",".........",".........","...XOX...","...O.O...","...XOX...",".........",".........","........."],"to_move":"white"},{"board":[".........",".........",".........",".........",".........",".........",".........",".........","........."],"to_move":"white"}],"activations":false}{"ok": true, "pass": 37, "kind": "positions", "positions": 9, "top3_per_position": [[["(5,4)", 1.0], ["(4,2)", 0.0], ["(4,6)", 0.0]], [["(5,4)", 1.0], ["(2,4)", 0.0], ["(4,2)", 0.0]], [["(4,4)", 1.0], ["(2,2)", 0.0], ["(5,2)", 0.0]], [["(4,4)", 1.0], ["(3,6)", 0.0], ["(6,3)", 0.0]], [["pass", 1.0], ["(4,6)", 0.0], ["(4,4)", 0.0]], [["(2,6)", 0.4972], ["(6,6)", 0.4972], ["(3,5)", 0.0012]], [["(4,6)", 1.0], ["(2,6)", 0.0], ["(6,6)", 0.0]], [["(2,3)", 0.1248], ["(3,2)", 0.1248], ["(2,5)", 0.1248]], [["(2,6)", 0.0977], ["(2,2)", 0.0977], ["(6,6)", 0.0977]]], "files": "/work/pass_37/", "readme": "inputs.npy (P, 243) the network inputs; legal.npy (P, 82) the legal moves (for 'board' positions: the empty points and pass; suicide and ko are not checked); logits.npy, probs.npy (P, 82); no activations (ask with \"activations\": true)."}import json, hashlib, numpy as np
names = ["L%d.%s"%(k,t) for k in range(1,7) for t in ["W","b"]]names += ["out.W","out.b"]tensors = {n:load(n) for n in names}reference = np.load("/work/candidate_A.npz")assert all(np.array_equal(tensors[n],reference[n]) for n in names)assert all(np.isfinite(a).all() for a in tensors.values())
# Static symmetry checks on weights only; no network evaluation.errors=[]for reflect in [False,True]: for turns in range(4): perm=[] for p in range(81): r,c=divmod(p,9) if reflect: c=8-c for _ in range(turns): r,c=c,8-r perm.append(9*r+c) perm=np.array(perm) ip=np.array([3*perm[p]+ch for p in range(81) for ch in range(3)]) hp=np.concatenate([81*ch+perm for ch in range(6)]+[np.arange(486,512)]) op=np.r_[perm,81] err=0.0 for k in range(1,7): w=tensors["L%d.W"%k]; bb=tensors["L%d.b"%k] rp=ip if k==1 else hp err=max(err,float(np.max(np.abs(w[np.ix_(rp,hp)]-w)))) err=max(err,float(np.max(np.abs(bb[hp]-bb)))) err=max(err,float(np.max(np.abs(tensors["out.W"][np.ix_(hp,op)]-tensors["out.W"])))) err=max(err,float(np.max(np.abs(tensors["out.b"][op]-tensors["out.b"])))) errors.append(err)
black=np.load("/work/pass_36/logits.npy")white=np.load("/work/pass_37/logits.npy")print("Checkpoint unchanged:", True)print("Eight board-symmetry weight errors:", errors)print("Colour-swapped placement-logit error:",float(np.max(np.abs(black[:,:81]-white[:,:81]))))print("White pass-logit increments:",np.round(white[:,81]-black[:,81],6).tolist())print("Nonzero weights/biases:",sum(np.count_nonzero(a) for a in tensors.values()))digest=hashlib.sha256()for n in names: digest.update(n.encode()) digest.update(tensors[n].tobytes())print("Checkpoint SHA256:",digest.hexdigest())json.dump({"checkpoint":digest.hexdigest(),"validation_passes":[28,31,32,33,34,35]}, open("/work/final_validation_manifest.json","w"),indent=2)Checkpoint unchanged: TrueEight board-symmetry weight errors: [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]Colour-swapped placement-logit error: 0.0White pass-logit increments: [8.375, 8.375, 8.375, 8.375, 8.375, 8.375, 8.375, 8.375, 8.375]Nonzero weights/biases: 25947Checkpoint SHA256: dc92ab2b220254b8a377493bfb892d5c03c6e6fa69c8c370053a8328a076ea54import numpy as np# The colour plane is broadcast over all 81 points. Reading it at one# fixed point is functionally invariant, although its raw weight row is not.errors=[]for reflect in [False,True]: for turns in range(4): perm=[] for p in range(81): r,c=divmod(p,9) if reflect: c=8-c for _ in range(turns): r,c=c,8-r perm.append(9*r+c) perm=np.asarray(perm) hp=np.concatenate([81*ch+perm for ch in range(6)]+[np.arange(486,512)]) op=np.r_[perm,81] w=load("L1.W") stone_rows=np.array([3*p+ch for p in range(81) for ch in [0,1]]) rotated_rows=np.array([3*perm[p]+ch for p in range(81) for ch in [0,1]]) err=float(np.max(np.abs(w[np.ix_(rotated_rows,hp)]-w[stone_rows]))) colour_weights=w[2::3].sum(axis=0) err=max(err,float(np.max(np.abs(colour_weights[hp]-colour_weights)))) for k in range(1,7): bb=load("L%d.b"%k) err=max(err,float(np.max(np.abs(bb[hp]-bb)))) if k>1: ww=load("L%d.W"%k) err=max(err,float(np.max(np.abs(ww[np.ix_(hp,hp)]-ww)))) ww=load("out.W"); bb=load("out.b") err=max(err,float(np.max(np.abs(ww[np.ix_(hp,op)]-ww))), float(np.max(np.abs(bb[op]-bb)))) errors.append(err)assert max(errors)==0print("Exact symmetry on all valid broadcast-colour inputs:",errors)print("No weights changed.")Exact symmetry on all valid broadcast-colour inputs: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]No weights changed.{"games":{"strong":128}}{"ok": true, "pass": 38, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.828125, "wins": 106, "losses": 22, "score_as_black": 0.765625, "score_as_white": 0.890625, "mean_margin": 53.046875, "mean_length": 117.9609375, "stones_captured_per_game": 63.6171875, "stones_lost_per_game": 11.0390625, "passes_per_game": 1.7421875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 3, "end_move_limit": 0}}, "files": "/work/pass_38/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 39, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8203125, "wins": 105, "losses": 23, "score_as_black": 0.734375, "score_as_white": 0.90625, "mean_margin": 50.578125, "mean_length": 115.75, "stones_captured_per_game": 59.9765625, "stones_lost_per_game": 9.84375, "passes_per_game": 1.8828125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 123, "end_repetition": 5, "end_move_limit": 0}}, "files": "/work/pass_39/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 40, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.7890625, "wins": 101, "losses": 27, "score_as_black": 0.78125, "score_as_white": 0.796875, "mean_margin": 51.0078125, "mean_length": 117.125, "stones_captured_per_game": 61.2109375, "stones_lost_per_game": 11.8984375, "passes_per_game": 1.6328125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 3, "end_move_limit": 0}}, "files": "/work/pass_40/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 41, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.859375, "wins": 110, "losses": 18, "score_as_black": 0.8125, "score_as_white": 0.90625, "mean_margin": 52.8125, "mean_length": 117.734375, "stones_captured_per_game": 64.5625, "stones_lost_per_game": 10.5703125, "passes_per_game": 1.7734375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 2, "end_move_limit": 1}}, "files": "/work/pass_41/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 42, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.828125, "wins": 106, "losses": 22, "score_as_black": 0.78125, "score_as_white": 0.875, "mean_margin": 49.703125, "mean_length": 116.703125, "stones_captured_per_game": 60.75, "stones_lost_per_game": 11.921875, "passes_per_game": 1.671875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 123, "end_repetition": 3, "end_move_limit": 2}}, "files": "/work/pass_42/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 43, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8203125, "wins": 105, "losses": 23, "score_as_black": 0.765625, "score_as_white": 0.875, "mean_margin": 51.5234375, "mean_length": 114.515625, "stones_captured_per_game": 58.7109375, "stones_lost_per_game": 10.59375, "passes_per_game": 1.46875, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 3, "end_move_limit": 0}}, "files": "/work/pass_43/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 44, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8359375, "wins": 107, "losses": 21, "score_as_black": 0.75, "score_as_white": 0.921875, "mean_margin": 52.0234375, "mean_length": 116.25, "stones_captured_per_game": 60.5625, "stones_lost_per_game": 10.125, "passes_per_game": 1.515625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 126, "end_repetition": 1, "end_move_limit": 1}}, "files": "/work/pass_44/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 45, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8125, "wins": 104, "losses": 24, "score_as_black": 0.8125, "score_as_white": 0.8125, "mean_margin": 51.640625, "mean_length": 118.078125, "stones_captured_per_game": 63.6328125, "stones_lost_per_game": 11.6640625, "passes_per_game": 2.0, "games_with_a_pass_before_move_60": 0, "end_two_passes": 124, "end_repetition": 2, "end_move_limit": 2}}, "files": "/work/pass_45/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 46, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.875, "wins": 112, "losses": 16, "score_as_black": 0.828125, "score_as_white": 0.921875, "mean_margin": 57.5859375, "mean_length": 118.9296875, "stones_captured_per_game": 68.171875, "stones_lost_per_game": 9.1796875, "passes_per_game": 1.6484375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 128, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_46/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":128}}{"ok": true, "pass": 47, "kind": "games", "per_opponent": {"strong": {"games": 128, "score": 0.8203125, "wins": 105, "losses": 23, "score_as_black": 0.765625, "score_as_white": 0.875, "mean_margin": 52.0078125, "mean_length": 115.8671875, "stones_captured_per_game": 60.3828125, "stones_lost_per_game": 10.1796875, "passes_per_game": 1.7265625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 125, "end_repetition": 2, "end_move_limit": 1}}, "files": "/work/pass_47/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"strong":112,"weak":16}}{"ok": true, "pass": 48, "kind": "games", "per_opponent": {"strong": {"games": 112, "score": 0.8392857313156128, "wins": 94, "losses": 18, "score_as_black": 0.7857142686843872, "score_as_white": 0.8928571343421936, "mean_margin": 53.13393020629883, "mean_length": 115.11607142857143, "stones_captured_per_game": 58.964285714285715, "stones_lost_per_game": 8.330357142857142, "passes_per_game": 1.6964285714285714, "games_with_a_pass_before_move_60": 0, "end_two_passes": 109, "end_repetition": 2, "end_move_limit": 1}, "weak": {"games": 16, "score": 1.0, "wins": 16, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 66.1875, "mean_length": 115.6875, "stones_captured_per_game": 67.3125, "stones_lost_per_game": 1.375, "passes_per_game": 1.9375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 16, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_48/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}{"games":{"weak":64,"random":64}}{"ok": true, "pass": 49, "kind": "games", "per_opponent": {"random": {"games": 64, "score": 1.0, "wins": 64, "losses": 0, "score_as_black": 1.0, "score_as_white": 1.0, "mean_margin": 66.515625, "mean_length": 111.9375, "stones_captured_per_game": 59.8125, "stones_lost_per_game": 0.375, "passes_per_game": 1.109375, "games_with_a_pass_before_move_60": 0, "end_two_passes": 64, "end_repetition": 0, "end_move_limit": 0}, "weak": {"games": 64, "score": 0.984375, "wins": 63, "losses": 1, "score_as_black": 0.96875, "score_as_white": 1.0, "mean_margin": 68.515625, "mean_length": 115.578125, "stones_captured_per_game": 69.90625, "stones_lost_per_game": 1.828125, "passes_per_game": 1.65625, "games_with_a_pass_before_move_60": 0, "end_two_passes": 62, "end_repetition": 2, "end_move_limit": 0}}, "files": "/work/pass_49/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}import json, hashlib, numpy as np
passes=[28,31,32,33,34,35]+list(range(38,50))all_games=[]for k in passes: all_games.extend(json.load(open("/work/pass_%d/games.json"%k)))
summary={}for opponent in ["strong","weak","random"]: games=[g for g in all_games if g["opponent"]==opponent] wins=sum(g["result"]>0 for g in games) n=len(games) p=wins/n z=1.959963984540054 den=1+z*z/n center=(p+z*z/(2*n))/den half=z*np.sqrt(p*(1-p)/n+z*z/(4*n*n))/den summary[opponent]={ "games":n, "wins":wins, "win_rate":p, "mean_margin":float(np.mean([g["margin"] for g in games])), "wilson_95_percent":[center-half,center+half], "by_colour":{ colour:{ "games":sum(g["net_color"]==colour for g in games), "wins":sum(g["net_color"]==colour and g["result"]>0 for g in games) } for colour in ["black","white"] } }
names=["L%d.%s"%(k,t) for k in range(1,7) for t in ["W","b"]]+["out.W","out.b"]reference=np.load("/work/candidate_A.npz")tensors={name:load(name).astype(np.float32,copy=False) for name in names}assert sum(a.size for a in tensors.values())==1480274assert all(np.isfinite(a).all() for a in tensors.values())assert all(np.array_equal(tensors[name],reference[name]) for name in names)
# Re-save the already validated checkpoint without changing any values.for name,array in tensors.items(): save(name,array)np.savez("/work/final_policy.npz",**tensors)
cfg=json.load(open("/work/candidate_A_config.json"))json.dump(cfg,open("/work/final_policy_config.json","w"),indent=2)open("/work/final_policy_design.py","w").write( open("/work/policy_design_v23.py").read())
digest=hashlib.sha256()for name in names: digest.update(name.encode()) digest.update(tensors[name].tobytes())manifest={ "sha256":digest.hexdigest(), "completed_validation_passes":passes, "pending_final_pass":50, "summary_before_final_pass":summary, "training_or_search_used":False}json.dump(manifest,open("/work/final_validation_manifest.json","w"),indent=2)print(json.dumps(summary,indent=2))print("All 14 final tensors saved, unchanged from candidate A.")print("SHA256:",digest.hexdigest())print("Final remaining pass will validate another 128 games against weak."){ "strong": { "games": 2000, "wins": 1667, "win_rate": 0.8335, "mean_margin": 52.811, "wilson_95_percent": [ 0.8165372993335623, 0.849184030129842 ], "by_colour": { "black": { "games": 1000, "wins": 787 }, "white": { "games": 1000, "wins": 880 } } }, "weak": { "games": 176, "wins": 175, "win_rate": 0.9943181818181818, "mean_margin": 69.61363636363636, "wilson_95_percent": [ 0.9685225276336739, 0.9989963144675896 ], "by_colour": { "black": { "games": 88, "wins": 87 }, "white": { "games": 88, "wins": 88 } } }, "random": { "games": 128, "wins": 128, "win_rate": 1.0, "mean_margin": 65.8515625, "wilson_95_percent": [ 0.9708630437264916, 1.0 ], "by_colour": { "black": { "games": 64, "wins": 64 }, "white": { "games": 64, "wins": 64 } } }}All 14 final tensors saved, unchanged from candidate A.SHA256: dc92ab2b220254b8a377493bfb892d5c03c6e6fa69c8c370053a8328a076ea54Final remaining pass will validate another 128 games against weak.{"games":{"weak":128}}{"ok": true, "pass": 50, "kind": "games", "per_opponent": {"weak": {"games": 128, "score": 0.9765625, "wins": 125, "losses": 3, "score_as_black": 0.953125, "score_as_white": 1.0, "mean_margin": 69.8984375, "mean_length": 117.6171875, "stones_captured_per_game": 71.84375, "stones_lost_per_game": 2.125, "passes_per_game": 1.78125, "games_with_a_pass_before_move_60": 0, "end_two_passes": 128, "end_repetition": 0, "end_move_limit": 0}}, "files": "/work/pass_50/", "readme": "games.json: one entry per game (opponent, net_color, result +1/-1, margin = net's area score minus the opponent's incl. komi, end, stones captured / lost, moves: ply, by, move (row*9+col, 81 = pass), and for the network's moves its top-5 [move, probability]). boards.npy: (games, 162, 81) int8, the board AFTER each ply (1 Black, -1 White, 0 empty; index row*9+col); valid.npy: (games, 162) which plies were played."}