File size: 2,724 Bytes
a7461aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(channels)
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(channels)
def forward(self, x):
residual = x
x = F.relu(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
return F.relu(x + residual)
class UltimateTicTacToeModel(nn.Module):
def __init__(self, board_size, action_size, device, channels=64, num_blocks=6):
super().__init__()
self.action_size = action_size
self.input_shape = board_size
self.input_channels = board_size[0]
self.board_height = board_size[1]
self.board_width = board_size[2]
self.device = torch.device(device)
self.stem = nn.Sequential(
nn.Conv2d(self.input_channels, channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(channels),
nn.ReLU(inplace=True),
)
self.residual_tower = nn.Sequential(*(ResidualBlock(channels) for _ in range(num_blocks)))
self.policy_head = nn.Sequential(
nn.Conv2d(channels, 32, kernel_size=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
)
self.policy_fc = nn.Linear(32 * self.board_height * self.board_width, self.action_size)
self.value_head = nn.Sequential(
nn.Conv2d(channels, 32, kernel_size=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
)
self.value_fc1 = nn.Linear(32 * self.board_height * self.board_width, 128)
self.value_fc2 = nn.Linear(128, 1)
self.to(self.device)
def forward(self, x):
x = x.view(-1, *self.input_shape)
x = self.stem(x)
x = self.residual_tower(x)
policy = self.policy_head(x)
policy = torch.flatten(policy, 1)
policy = self.policy_fc(policy)
value = self.value_head(x)
value = torch.flatten(value, 1)
value = F.relu(self.value_fc1(value))
value = torch.tanh(self.value_fc2(value))
return F.softmax(policy, dim=1), value
def predict(self, board):
board = torch.as_tensor(board, dtype=torch.float32, device=self.device)
board = board.view(1, *self.input_shape)
self.eval()
with torch.no_grad():
pi, v = self.forward(board)
return pi.detach().cpu().numpy()[0], float(v.item())
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