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TASK. Write, from scratch, a policy network that plays 9 x 9 Go as strongly as possible. All of its weights start at zero; you set them. The goal is to win as many games as possible against the opponents described under HOW IT WILL BE MEASURED. THE GAME: Go on a 9 x 9 board (the pgx implementation, "go_9x9"). Rows are numbered 0 (top) to 8 (bottom), columns 0 (left) to 8 (right). Black moves first; players alternate. A move either places a stone of the mover's colour on an empty point or passes. A group of stones with no adjacent empty point is captured and removed. Suicide is illegal, and so is retaking a single stone immediately (simple ko). A move that repeats an earlier whole-board position ends the game at once, and the player who made it loses. The game ends after two consecutive passes, or after 162 moves in total. Then it is scored by area: each player's stones on the board plus the empty points surrounded only by that player's stones; White receives a komi of 7.5 points. The higher score wins (no draws). THE NETWORK. Input: 243 numbers, each 0 or 1: three planes over the 81 points, from the point of view of the player to move; input number (row * 9 + column) * 3 + plane, where plane 0 is 1 where the player to move has a stone, plane 1 is 1 where the opponent has a stone, and plane 2 is 1 everywhere when White is to move (0 when Black is). Output: 82 logits: output row * 9 + column = play at that point, output 81 = pass. h_1 = relu(x @ L1.W + L1.b) (512 units) h_k = relu(h_(k-1) @ Lk.W + Lk.b) for k = 2..6 (512 units each) logits = h_6 @ out.W + out.b (82) Tensors: L1.W (243 x 512), L1.b (512), L2.W .. L6.W (512 x 512), L2.b .. L6.b (512), out.W (512 x 82), out.b (82); 1,480,274 numbers. Row i of a weight matrix belongs to input (or unit) i, column j to unit (or output) j. Only the values can be set; the architecture is fixed. Illegal moves (occupied points, suicide, simple ko) are removed before every move, and the move is sampled from the softmax of the remaining logits (temperature 1). There is no search: the network's output is the whole policy. HOW IT WILL BE MEASURED. After you finish, your network plays 2,000 games against each of several hidden opponents: policy networks trained with reinforcement learning on this game (self-play; they beat a uniform random player 79-98 % of the time), and a uniform random player. Your network is Black in half of the games; the moves of both players are sampled from their policies; there is no search. Score: wins / games; the main measure is the average over the trained opponents. You never see these opponents. WHAT YOU HAVE. 1. A Python sandbox (no internet, no GPU, no Go library) with a working folder /work that is kept between runs. Available: numpy, scipy, scikit-learn, PyTorch (CPU), pandas, sympy, networkx. Before your code runs, these are defined: SHAPES and NAMES (the tensors), load(name) (a float32 numpy array: the values you saved, zeros at the start), save(name, array) (stores a tensor of the same shape), saved() (the names saved so far), inp(row, col, plane) (the input index), mv(row, col) (the output index) and PASS = 81. A run may take up to 900 seconds and 96 GB; its printed output (cut at 20,000 characters) is sent back to you. To run code, reply with exactly one block that starts with ```python and nothing else. You may run code up to 120 times. 2. No pass tools (budget 0): your network cannot be played or run before the evaluation. Work from your design alone. At the end, the tensors you saved with save() are your network; tensors never saved stay zero. RULES. Not allowed: (a) gradient descent or any other training: no backward passes, autograd or optimizers, and no fitting of weights to data or labels (no regression or least squares on examples); (b) search: no game-tree search (MCTS, minimax, rollouts, looking ahead over sequences of moves), neither to choose moves nor to produce target moves or values for setting weights; (c) running the network outside the pass tools: do not compute your network's output or activations on any input in the sandbox; (d) internet access (there is none). Allowed: writing the weights by code from your own design (formulas, loops, symmetries, linear algebra on the weights themselves), Go-rule code in the sandbox (for example to work out the liberties of groups on boards you choose) as long as it does not run the network or search, and any analysis of the pass results. HOW TO FINISH. When your network is saved, reply WITHOUT a code block: describe its design and why you expect it to win.
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.
```python
import numpy as np
# Hand-designed policy circuit. No training, game simulation, or network execution.
N = 81
H = 512
OFFSET = 128.0
params = {}
for k in range(1, 7):
params[f"L{k}.W"] = np.zeros((243 if k == 1 else H, H), np.float32)
params[f"L{k}.b"] = np.zeros(H, np.float32)
params["out.W"] = np.zeros((H, 82), np.float32)
params["out.b"] = np.zeros(82, np.float32)
def unit(channel, point):
return channel * N + point
def point(r, c):
return r * 9 + c
coords = [divmod(p, 9) for p in range(N)]
neighbors = []
diagonals = []
for r, c in coords:
neighbors.append([
point(r + dr, c + dc)
for dr, dc in ((-1, 0), (1, 0), (0, -1), (0, 1))
if 0 <= r + dr < 9 and 0 <= c + dc < 9
])
diagonals.append([
point(r + dr, c + dc)
for dr, dc in ((-1, -1), (-1, 1), (1, -1), (1, 1))
if 0 <= r + dr < 9 and 0 <= c + dc < 9
])
# Density, friendly shape, and opponent approach kernels.
kernels = {
(0, 1): (1.000, 0.550, -0.650),
(1, 1): (0.750, 0.700, 0.100),
(0, 2): (0.700, 0.750, 0.250),
(1, 2): (0.420, 0.450, 0.120),
(2, 2): (0.180, 0.120, 0.000),
(0, 3): (0.200, 0.200, 0.030),
(1, 3): (0.120, 0.120, 0.020),
(2, 3): (0.050, 0.040, 0.000),
(3, 3): (0.025, 0.015, 0.000),
(0, 4): (0.055, 0.030, 0.000),
(1, 4): (0.035, 0.020, 0.000),
(2, 4): (0.015, 0.000, 0.000),
}
density = np.zeros((N, N), np.float32)
friendly_shape = np.zeros((N, N), np.float32)
enemy_shape = np.zeros((N, N), np.float32)
full_density_mass = 0.0
for dr in range(-4, 5):
for dc in range(-4, 5):
full_density_mass += kernels.get(
tuple(sorted((abs(dr), abs(dc)))), (0.0, 0.0, 0.0)
)[0]
for p, (r, c) in enumerate(coords):
for q, (s, t) in enumerate(coords):
d, f, e = kernels.get(
tuple(sorted((abs(r - s), abs(c - t)))),
(0.0, 0.0, 0.0),
)
density[p, q] = d
friendly_shape[p, q] = f
enemy_shape[p, q] = e
factor = min(
1.65,
np.sqrt(full_density_mass / float(density[p].sum()))
)
density[p] *= factor
TOTAL, WHITE, OWN_COUNT, OPP_COUNT = 486, 487, 488, 489
PHASE, LATE, AHEAD, BEHIND = 490, 491, 492, 493
# L1: stones, direct liberties on stones, and territorial density penalties.
W, b = params["L1.W"], params["L1.b"]
for p in range(N):
W[3*p, unit(0, p)] = 1.0
W[3*p+1, unit(1, p)] = 1.0
for color, channel in ((0, 2), (1, 3)):
j = unit(channel, p)
b[j] = len(neighbors[p]) - 4.0
W[3*p+color, j] += 4.0
for q in neighbors[p]:
W[3*q, j] -= 1.0
W[3*q+1, j] -= 1.0
j_own, j_opp = unit(4, p), unit(5, p)
b[j_own], b[j_opp] = -2.4, -2.1
for q in range(N):
d = density[p, q]
if d:
W[3*q, j_own] += d
W[3*q+1, j_own] -= 0.7*d
W[3*q+1, j_opp] += d
W[3*q, j_opp] -= 0.7*d
W[3*p, TOTAL] = 1.0
W[3*p+1, TOTAL] = 1.0
W[3*p+2, WHITE] = 1.0 / N
W[3*p, OWN_COUNT] = 1.0
W[3*p+1, OPP_COUNT] = 1.0
# L2: certified liberty evidence and strategic move scores.
#
# In a same-color star, an individual liberty contributes at most eight
# to the weighted liberty sum below. Thus sums >8 and >16 certify
# at least two and three DISTINCT liberties, respectively.
W, b = params["L2.W"], params["L2.b"]
line_prior = [-1.30, -0.05, 0.75, 0.65, 0.55,
0.65, 0.75, -0.05, -1.30]
for p, (r, c) in enumerate(coords):
W[unit(0, p), unit(0, p)] = 1.0
W[unit(1, p), unit(1, p)] = 1.0
# The orientation choice preserves rotations and reflections.
if abs(r - 4) > abs(c - 4):
vertical, horizontal = 3.0, 5.0
elif abs(r - 4) < abs(c - 4):
vertical, horizontal = 5.0, 3.0
else:
vertical = horizontal = 4.0
for color, source_lib, destination, threshold in (
(0, 2, 2, 8.0),
(1, 3, 3, 8.0),
(0, 2, 4, 16.0),
):
j = unit(destination, p)
b[j] = -threshold - 64.0
W[unit(color, p), j] += 64.0
W[unit(source_lib, p), j] += 8.0
for q in neighbors[p]:
s, t = coords[q]
coefficient = vertical if s != r else horizontal
W[unit(source_lib, q), j] += coefficient
j = unit(5, p)
positional = line_prior[r] + line_prior[c]
if r in (2, 6) and c in (2, 6):
positional += 0.35
if (r, c) == (4, 4):
positional += 0.15
b[j] = OFFSET + positional
W[unit(4, p), j] -= 2.4
W[unit(5, p), j] -= 2.7
for q in range(N):
W[unit(0, q), j] += friendly_shape[p, q]
W[unit(1, q), j] += enemy_shape[p, q]
for g in (TOTAL, WHITE, OWN_COUNT, OPP_COUNT):
W[g, g] = 1.0
W[TOTAL, PHASE], b[PHASE] = 1.0, -18.0
W[TOTAL, LATE], b[LATE] = 1.0, -52.0
W[OWN_COUNT, AHEAD] = 1.0
W[OPP_COUNT, AHEAD] = -1.0
W[WHITE, AHEAD] = 7.5
b[AHEAD] = -1.0
W[OWN_COUNT, BEHIND] = -1.0
W[OPP_COUNT, BEHIND] = 1.0
W[WHITE, BEHIND] = -7.5
b[BEHIND] = 1.0
def copy_globals(W):
for g in range(TOTAL, BEHIND + 1):
W[g, g] = 1.0
# L3: propagate certified liberty evidence through same-color stones.
# A pair (z,g) encodes stone=z-g exactly and nonnegative evidence=g.
W, b = params["L3.W"], params["L3.b"]
M = 481.0
for p in range(N):
neighborhood = [p] + neighbors[p]
for stone_channel, evidence_channel, z_channel, g_channel in (
(0, 2, 0, 1),
(1, 3, 2, 3),
):
for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):
j = unit(destination, p)
W[unit(stone_channel, p), j] += M + 1.0
for q in neighborhood:
W[unit(evidence_channel, q), j] += 1.0
b[j] = -M - subtraction
j = unit(4, p)
W[unit(0, p), j] += M
for q in neighborhood:
W[unit(4, q), j] += 1.0
b[j] = -M
W[unit(5, p), unit(5, p)] = 1.0
copy_globals(W)
# L4: another same-color propagation step.
W, b = params["L4.W"], params["L4.b"]
M = 2401.0
for p in range(N):
neighborhood = [p] + neighbors[p]
for z_channel, g_channel in ((0, 1), (2, 3)):
for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):
j = unit(destination, p)
W[unit(z_channel, p), j] += M + 1.0
W[unit(g_channel, p), j] -= M + 1.0
for q in neighborhood:
W[unit(g_channel, q), j] += 1.0
b[j] = -M - subtraction
j = unit(4, p)
W[unit(0, p), j] += M
W[unit(1, p), j] -= M
for q in neighborhood:
W[unit(4, q), j] += 1.0
b[j] = -M
W[unit(5, p), unit(5, p)] = 1.0
copy_globals(W)
# L5: recover stones and bounded tactical classifications.
# "Unsafe" means no two-liberty certificate reached this stone.
# These flags deliberately err toward noticing threats.
W, b = params["L5.W"], params["L5.b"]
for p in range(N):
neighborhood = [p] + neighbors[p]
for z_channel, g_channel, stone_channel, unsafe_channel in (
(0, 1, 0, 2),
(2, 3, 1, 3),
):
j = unit(stone_channel, p)
W[unit(z_channel, p), j] += 1.0
W[unit(g_channel, p), j] -= 1.0
j = unit(unsafe_channel, p)
W[unit(z_channel, p), j] += 1.0
W[unit(g_channel, p), j] -= 1.0
for q in neighborhood:
W[unit(g_channel, q), j] -= 1.0
j = unit(4, p) # Friendly stone lacking a three-liberty certificate.
W[unit(0, p), j] += 1.0
W[unit(1, p), j] -= 1.0
for q in neighborhood:
W[unit(4, q), j] -= 1.0
W[unit(5, p), unit(5, p)] = 1.0
copy_globals(W)
# L6: move-level capture/rescue, shape, eyes, self-atari, and safe contact.
W, b = params["L6.W"], params["L6.b"]
for p, (r, c) in enumerate(coords):
degree = len(neighbors[p])
for q in neighbors[p]:
W[unit(2, q), unit(0, p)] += 1.0 # Rescue urgency.
W[unit(3, q), unit(1, p)] += 1.0 # Capture urgency.
j = unit(2, p)
W[unit(5, p), j] = 1.0
edge_phase_weight = 0.0
for d in (min(r, 8-r), min(c, 8-c)):
if d == 0:
edge_phase_weight += 0.030
elif d == 1:
edge_phase_weight += 0.012
W[PHASE, j] += edge_phase_weight
for q in neighbors[p]:
W[unit(1, q), j] += 1.1
# True-eye preservation, with diagonal false-eye exceptions.
j = unit(3, p)
b[j] = 1.0 - degree
for q in neighbors[p]:
W[unit(0, q), j] += 1.0
diagonal_penalty = 0.5 if degree == 4 else 1.0
for q in diagonals[p]:
W[unit(1, q), j] -= diagonal_penalty
# Conservative self-atari / futile-extension penalty.
# Capturing or connecting to a certified strong group exempts a move.
j = unit(4, p)
b[j] = 2.0 - 1.6 * degree
for q in neighbors[p]:
W[unit(0, q), j] += -3.2
W[unit(1, q), j] += 1.6
W[unit(2, q), j] += 1.4
W[unit(3, q), j] -= 4.0
W[unit(4, q), j] += 4.0
# Enemy contact is rewarded primarily when backed by a strong group.
j = unit(5, p)
for q in neighbors[p]:
W[unit(1, q), j] += 1.0
W[unit(0, q), j] -= 2.0
W[unit(4, q), j] += 2.0
copy_globals(W)
# Output policy.
W, b = params["out.W"], params["out.b"]
for p in range(N):
b[p] = -OFFSET
for channel, coefficient in (
(0, 4.6),
(1, 7.1),
(2, 1.0),
(3, -64.0),
(4, -9.0),
(5, -1.6),
):
W[unit(channel, p), p] = coefficient
# Passing remains unlikely while constructive moves exist.
b[81] = -5.6
W[PHASE, 81] = 0.050
W[LATE, 81] = 0.045
W[AHEAD, 81] = 0.025
W[BEHIND, 81] = -0.035
W[WHITE, 81] = 0.150
# Only inspect and save weights; do not evaluate the policy.
for name, array in params.items():
assert array.dtype == np.float32
assert np.isfinite(array).all()
save(name, array)
print("Saved all 14 tensors.")
print("Parameters:", sum(a.size for a in params.values()))
print("Nonzero parameters:", sum(np.count_nonzero(a) for a in params.values()))
print("Design: certified local liberty propagation, capture/rescue priorities,")
print("self-atari filtering, eye preservation, territorial shape, and late passing.")
```{
"t": 1792788733.1529126,
"kind": "python",
"s": 1.25,
"code": "import numpy as np\n\n# Hand-designed policy circuit. No training, game simulation, or network execution.\nN = 81\nH = 512\nOFFSET = 128.0\n\nparams = {}\nfor k in range(1, 7):\n params[f\"L{k}.W\"] = np.zeros((243 if k == 1 else H, H), np.float32)\n params[f\"L{k}.b\"] = np.zeros(H, np.float32)\nparams[\"out.W\"] = np.zeros((H, 82), np.float32)\nparams[\"out.b\"] = np.zeros(82, np.float32)\n\ndef unit(channel, point):\n return channel * N + point\n\ndef point(r, c):\n return r * 9 + c\n\ncoords = [divmod(p, 9) for p in range(N)]\nneighbors = []\ndiagonals = []\nfor r, c in coords:\n neighbors.append([\n point(r + dr, c + dc)\n for dr, dc in ((-1, 0), (1, 0), (0, -1), (0, 1))\n if 0 <= r + dr < 9 and 0 <= c + dc < 9\n ])\n diagonals.append([\n point(r + dr, c + dc)\n for dr, dc in ((-1, -1), (-1, 1), (1, -1), (1, 1))\n if 0 <= r + dr < 9 and 0 <= c + dc < 9\n ])\n\n# Density, friendly shape, and opponent approach kernels.\nkernels = {\n (0, 1): (1.000, 0.550, -0.650),\n (1, 1): (0.750, 0.700, 0.100),\n (0, 2): (0.700, 0.750, 0.250),\n (1, 2): (0.420, 0.450, 0.120),\n (2, 2): (0.180, 0.120, 0.000),\n (0, 3): (0.200, 0.200, 0.030),\n (1, 3): (0.120, 0.120, 0.020),\n (2, 3): (0.050, 0.040, 0.000),\n (3, 3): (0.025, 0.015, 0.000),\n (0, 4): (0.055, 0.030, 0.000),\n (1, 4): (0.035, 0.020, 0.000),\n (2, 4): (0.015, 0.000, 0.000),\n}\n\ndensity = np.zeros((N, N), np.float32)\nfriendly_shape = np.zeros((N, N), np.float32)\nenemy_shape = np.zeros((N, N), np.float32)\n\nfull_density_mass = 0.0\nfor dr in range(-4, 5):\n for dc in range(-4, 5):\n full_density_mass += kernels.get(\n tuple(sorted((abs(dr), abs(dc)))), (0.0, 0.0, 0.0)\n )[0]\n\nfor p, (r, c) in enumerate(coords):\n for q, (s, t) in enumerate(coords):\n d, f, e = kernels.get(\n tuple(sorted((abs(r - s), abs(c - t)))),\n (0.0, 0.0, 0.0),\n )\n density[p, q] = d\n friendly_shape[p, q] = f\n enemy_shape[p, q] = e\n factor = min(\n 1.65,\n np.sqrt(full_density_mass / float(density[p].sum()))\n )\n density[p] *= factor\n\nTOTAL, WHITE, OWN_COUNT, OPP_COUNT = 486, 487, 488, 489\nPHASE, LATE, AHEAD, BEHIND = 490, 491, 492, 493\n\n# L1: stones, direct liberties on stones, and territorial density penalties.\nW, b = params[\"L1.W\"], params[\"L1.b\"]\nfor p in range(N):\n W[3*p, unit(0, p)] = 1.0\n W[3*p+1, unit(1, p)] = 1.0\n\n for color, channel in ((0, 2), (1, 3)):\n j = unit(channel, p)\n b[j] = len(neighbors[p]) - 4.0\n W[3*p+color, j] += 4.0\n for q in neighbors[p]:\n W[3*q, j] -= 1.0\n W[3*q+1, j] -= 1.0\n\n j_own, j_opp = unit(4, p), unit(5, p)\n b[j_own], b[j_opp] = -2.4, -2.1\n for q in range(N):\n d = density[p, q]\n if d:\n W[3*q, j_own] += d\n W[3*q+1, j_own] -= 0.7*d\n W[3*q+1, j_opp] += d\n W[3*q, j_opp] -= 0.7*d\n\n W[3*p, TOTAL] = 1.0\n W[3*p+1, TOTAL] = 1.0\n W[3*p+2, WHITE] = 1.0 / N\n W[3*p, OWN_COUNT] = 1.0\n W[3*p+1, OPP_COUNT] = 1.0\n\n# L2: certified liberty evidence and strategic move scores.\n#\n# In a same-color star, an individual liberty contributes at most eight\n# to the weighted liberty sum below. Thus sums >8 and >16 certify\n# at least two and three DISTINCT liberties, respectively.\nW, b = params[\"L2.W\"], params[\"L2.b\"]\nline_prior = [-1.30, -0.05, 0.75, 0.65, 0.55,\n 0.65, 0.75, -0.05, -1.30]\n\nfor p, (r, c) in enumerate(coords):\n W[unit(0, p), unit(0, p)] = 1.0\n W[unit(1, p), unit(1, p)] = 1.0\n\n # The orientation choice preserves rotations and reflections.\n if abs(r - 4) > abs(c - 4):\n vertical, horizontal = 3.0, 5.0\n elif abs(r - 4) < abs(c - 4):\n vertical, horizontal = 5.0, 3.0\n else:\n vertical = horizontal = 4.0\n\n for color, source_lib, destination, threshold in (\n (0, 2, 2, 8.0),\n (1, 3, 3, 8.0),\n (0, 2, 4, 16.0),\n ):\n j = unit(destination, p)\n b[j] = -threshold - 64.0\n W[unit(color, p), j] += 64.0\n W[unit(source_lib, p), j] += 8.0\n for q in neighbors[p]:\n s, t = coords[q]\n coefficient = vertical if s != r else horizontal\n W[unit(source_lib, q), j] += coefficient\n\n j = unit(5, p)\n positional = line_prior[r] + line_prior[c]\n if r in (2, 6) and c in (2, 6):\n positional += 0.35\n if (r, c) == (4, 4):\n positional += 0.15\n b[j] = OFFSET + positional\n\n W[unit(4, p), j] -= 2.4\n W[unit(5, p), j] -= 2.7\n for q in range(N):\n W[unit(0, q), j] += friendly_shape[p, q]\n W[unit(1, q), j] += enemy_shape[p, q]\n\nfor g in (TOTAL, WHITE, OWN_COUNT, OPP_COUNT):\n W[g, g] = 1.0\nW[TOTAL, PHASE], b[PHASE] = 1.0, -18.0\nW[TOTAL, LATE], b[LATE] = 1.0, -52.0\nW[OWN_COUNT, AHEAD] = 1.0\nW[OPP_COUNT, AHEAD] = -1.0\nW[WHITE, AHEAD] = 7.5\nb[AHEAD] = -1.0\nW[OWN_COUNT, BEHIND] = -1.0\nW[OPP_COUNT, BEHIND] = 1.0\nW[WHITE, BEHIND] = -7.5\nb[BEHIND] = 1.0\n\ndef copy_globals(W):\n for g in range(TOTAL, BEHIND + 1):\n W[g, g] = 1.0\n\n# L3: propagate certified liberty evidence through same-color stones.\n# A pair (z,g) encodes stone=z-g exactly and nonnegative evidence=g.\nW, b = params[\"L3.W\"], params[\"L3.b\"]\nM = 481.0\nfor p in range(N):\n neighborhood = [p] + neighbors[p]\n for stone_channel, evidence_channel, z_channel, g_channel in (\n (0, 2, 0, 1),\n (1, 3, 2, 3),\n ):\n for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):\n j = unit(destination, p)\n W[unit(stone_channel, p), j] += M + 1.0\n for q in neighborhood:\n W[unit(evidence_channel, q), j] += 1.0\n b[j] = -M - subtraction\n\n j = unit(4, p)\n W[unit(0, p), j] += M\n for q in neighborhood:\n W[unit(4, q), j] += 1.0\n b[j] = -M\n W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L4: another same-color propagation step.\nW, b = params[\"L4.W\"], params[\"L4.b\"]\nM = 2401.0\nfor p in range(N):\n neighborhood = [p] + neighbors[p]\n for z_channel, g_channel in ((0, 1), (2, 3)):\n for destination, subtraction in ((z_channel, 0.0), (g_channel, 1.0)):\n j = unit(destination, p)\n W[unit(z_channel, p), j] += M + 1.0\n W[unit(g_channel, p), j] -= M + 1.0\n for q in neighborhood:\n W[unit(g_channel, q), j] += 1.0\n b[j] = -M - subtraction\n\n j = unit(4, p)\n W[unit(0, p), j] += M\n W[unit(1, p), j] -= M\n for q in neighborhood:\n W[unit(4, q), j] += 1.0\n b[j] = -M\n W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L5: recover stones and bounded tactical classifications.\n# \"Unsafe\" means no two-liberty certificate reached this stone.\n# These flags deliberately err toward noticing threats.\nW, b = params[\"L5.W\"], params[\"L5.b\"]\nfor p in range(N):\n neighborhood = [p] + neighbors[p]\n for z_channel, g_channel, stone_channel, unsafe_channel in (\n (0, 1, 0, 2),\n (2, 3, 1, 3),\n ):\n j = unit(stone_channel, p)\n W[unit(z_channel, p), j] += 1.0\n W[unit(g_channel, p), j] -= 1.0\n\n j = unit(unsafe_channel, p)\n W[unit(z_channel, p), j] += 1.0\n W[unit(g_channel, p), j] -= 1.0\n for q in neighborhood:\n W[unit(g_channel, q), j] -= 1.0\n\n j = unit(4, p) # Friendly stone lacking a three-liberty certificate.\n W[unit(0, p), j] += 1.0\n W[unit(1, p), j] -= 1.0\n for q in neighborhood:\n W[unit(4, q), j] -= 1.0\n\n W[unit(5, p), unit(5, p)] = 1.0\ncopy_globals(W)\n\n# L6: move-level capture/rescue, shape, eyes, self-atari, and safe contact.\nW, b = params[\"L6.W\"], params[\"L6.b\"]\nfor p, (r, c) in enumerate(coords):\n degree = len(neighbors[p])\n\n for q in neighbors[p]:\n W[unit(2, q), unit(0, p)] += 1.0 # Rescue urgency.\n W[unit(3, q), unit(1, p)] += 1.0 # Capture urgency.\n\n j = unit(2, p)\n W[unit(5, p), j] = 1.0\n edge_phase_weight = 0.0\n for d in (min(r, 8-r), min(c, 8-c)):\n if d == 0:\n edge_phase_weight += 0.030\n elif d == 1:\n edge_phase_weight += 0.012\n W[PHASE, j] += edge_phase_weight\n for q in neighbors[p]:\n W[unit(1, q), j] += 1.1\n\n # True-eye preservation, with diagonal false-eye exceptions.\n j = unit(3, p)\n b[j] = 1.0 - degree\n for q in neighbors[p]:\n W[unit(0, q), j] += 1.0\n diagonal_penalty = 0.5 if degree == 4 else 1.0\n for q in diagonals[p]:\n W[unit(1, q), j] -= diagonal_penalty\n\n # Conservative self-atari / futile-extension penalty.\n # Capturing or connecting to a certified strong group exempts a move.\n j = unit(4, p)\n b[j] = 2.0 - 1.6 * degree\n for q in neighbors[p]:\n W[unit(0, q), j] += -3.2\n W[unit(1, q), j] += 1.6\n W[unit(2, q), j] += 1.4\n W[unit(3, q), j] -= 4.0\n W[unit(4, q), j] += 4.0\n\n # Enemy contact is rewarded primarily when backed by a strong group.\n j = unit(5, p)\n for q in neighbors[p]:\n W[unit(1, q), j] += 1.0\n W[unit(0, q), j] -= 2.0\n W[unit(4, q), j] += 2.0\n\ncopy_globals(W)\n\n# Output policy.\nW, b = params[\"out.W\"], params[\"out.b\"]\nfor p in range(N):\n b[p] = -OFFSET\n for channel, coefficient in (\n (0, 4.6),\n (1, 7.1),\n (2, 1.0),\n (3, -64.0),\n (4, -9.0),\n (5, -1.6),\n ):\n W[unit(channel, p), p] = coefficient\n\n# Passing remains unlikely while constructive moves exist.\nb[81] = -5.6\nW[PHASE, 81] = 0.050\nW[LATE, 81] = 0.045\nW[AHEAD, 81] = 0.025\nW[BEHIND, 81] = -0.035\nW[WHITE, 81] = 0.150\n\n# Only inspect and save weights; do not evaluate the policy.\nfor name, array in params.items():\n assert array.dtype == np.float32\n assert np.isfinite(array).all()\n save(name, array)\n\nprint(\"Saved all 14 tensors.\")\nprint(\"Parameters:\", sum(a.size for a in params.values()))\nprint(\"Nonzero parameters:\", sum(np.count_nonzero(a) for a in params.values()))\nprint(\"Design: certified local liberty propagation, capture/rescue priorities,\")\nprint(\"self-atari filtering, eye preservation, territorial shape, and late passing.\")\n",
"out": "Saved all 14 tensors.\nParameters: 1480274\nNonzero parameters: 34190\nDesign: certified local liberty propagation, capture/rescue priorities,\nself-atari filtering, eye preservation, territorial shape, and late passing.\n"
}Exact prompt · Raw CLI events · Settings and audit
OUTPUT of Python run 1 of at most 120: ``` Saved all 14 tensors. Parameters: 1480274 Nonzero parameters: 34190 Design: certified local liberty propagation, capture/rescue priorities, self-atari filtering, eye preservation, territorial shape, and late passing. ```
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.
Saved all 14 tensors. The network propagates certified liberty evidence through nearby chains, prioritizes captures and rescues, discourages self-atari, preserves eyes, and favors supported moves over isolated invasions. Positional preferences shift toward edge completion later, with conservative passing. These heuristics should exploit tactical mistakes while maintaining territory and living groups. Strength against the hidden opponents remains untested.
Saved all 14 tensors. The network propagates certified liberty evidence through nearby chains, prioritizes captures and rescues, discourages self-atari, preserves eyes, and favors supported moves over isolated invasions. Positional preferences shift toward edge completion later, with conservative passing. These heuristics should exploit tactical mistakes while maintaining territory and living groups. Strength against the hidden opponents remains untested.