+
+def generate_snake_sequences(
+ nb, height, width, nb_colors, length, device=torch.device("cpu")
+):
+ world = torch.randint(nb_colors, (nb, height, width), device=device)
+ # nb x 2
+ snake_position = torch.cat(
+ (
+ torch.randint(height, (nb, 1), device=device),
+ torch.randint(width, (nb, 1), device=device),
+ ),
+ 1,
+ )
+ snake_direction = torch.randint(4, (nb, 1), device=device)
+ result = torch.empty(nb, 2*length, device=device, dtype=torch.int64)
+ count = torch.arange(nb, device=device) # [:,None]
+
+ for l in range(length):
+ # nb x 3
+ snake_next_direction = torch.cat(
+ (
+ (snake_direction - 1) % 4,
+ snake_direction,
+ (snake_direction + 1) % 4,
+ ),
+ 1,
+ )
+
+ # nb x 3
+ vh = (snake_next_direction + 1) % 2 * (snake_next_direction - 1)
+ vw = snake_next_direction % 2 * (snake_next_direction - 2)
+
+ # nb x 3 x 2
+ snake_next_speed = torch.cat((vh[:, :, None], vw[:, :, None]), 2)
+ snake_next_position = snake_position[:, None, :] + snake_next_speed
+
+ # nb x 3
+ val = torch.logical_and(
+ torch.logical_and(
+ snake_next_position[:, :, 0] >= 0, snake_next_position[:, :, 0] < height
+ ),
+ torch.logical_and(
+ snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width
+ ),
+ ).float()
+ val = torch.rand_like(val) * val * torch.tensor([[1.,4.,1.]], device=device)
+
+ # nb
+ i = torch.arange(val.size(0), device=device)
+ j = val.argmax(1)
+
+ # nb x 1
+ snake_direction = snake_next_direction[i[:, None], j[:, None]]
+
+ result[:, 2*l] = world[count, snake_position[:, 0], snake_position[:, 1]]
+ result[:, 2*l+1] = snake_direction[:,0]
+
+ # nb x 2
+ snake_position = snake_next_position[i[:, None], j[:, None]].squeeze(1)
+
+ return result
+
+generate_snake_sequences(nb=2, height=4, width=5, nb_colors=3, length=10)
+exit(0)
+