######################################################################
-def mark_path(walls, i, j, goal_i, goal_j):
- policy = compute_policy(walls, goal_i, goal_j)
+def mark_path(walls, i, j, goal_i, goal_j, policy):
action = torch.distributions.categorical.Categorical(
policy.permute(1, 2, 0)
).sample()
- walls[i, j] = 4
n, nmax = 0, walls.numel()
while i != goal_i or j != goal_j:
di, dj = [(0, -1), (0, 1), (-1, 0), (1, 0)][action[i, j]]
i, j = i + di, j + dj
assert walls[i, j] == 0
- walls[i, j] = 4
+ walls[i, j] = v_path
n += 1
assert n < nmax
):
mazes = torch.empty(nb, height, width, dtype=torch.int64)
paths = torch.empty(nb, height, width, dtype=torch.int64)
+ policies = torch.empty(nb, 4, height, width, dtype=torch.int64)
for n in progress_bar(range(nb)):
maze = create_maze(height, width, nb_walls)
start, goal = i[torch.randperm(i.size(0))[:2]]
if (start - goal).abs().sum() >= dist_min:
break
+ start_i, start_j, goal_i, goal_j = start[0], start[1], goal[0], goal[1]
+ policy = compute_policy(maze, goal_i, goal_j)
path = maze.clone()
- mark_path(path, start[0], start[1], goal[0], goal[1])
- maze[start[0], start[1]] = v_start
- maze[goal[0], goal[1]] = v_goal
- path[start[0], start[1]] = v_start
- path[goal[0], goal[1]] = v_goal
+ mark_path(path, start_i, start_j, goal_i, goal_j, policy)
+ maze[start_i, start_j] = v_start
+ maze[goal_i, goal_j] = v_goal
+ path[start_i, start_j] = v_start
+ path[goal_i, goal_j] = v_goal
mazes[n] = maze
paths[n] = path
+ policies[n] = policy
- return mazes, paths
+ return mazes, paths, policies
######################################################################
######################################################################
if __name__ == "__main__":
-
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
mazes, paths = create_maze_data(8)
mazes, paths = mazes.to(device), paths.to(device)