def val_run(discount_factor=0.9):
maze = read_maze(maze_file)
env = MazeEnv(maze_file=maze_file)
obs = env.reset()
policy, V, num_iter, total_access = value_iteration(
env, discount_factor=discount_factor)
coord, action = np.where(policy == 1)
policy = action.reshape(maze_shape)
V = V.reshape(maze_shape)
im = graph_value_policy(V, policy, maze)
return im
# im = graph_value_policy(V, policy, maze)
# plt.colorbar()
# # print(np.sum(VV - VP))
# # print(VV[5][5])
# # print(VP[5][5])
# print(p_val)
# print(p_pol)
# discount_factor=0.9
df = 0.9
for df in discount_factors:
plt.subplots(12)
plt.subplot(121)
maze = read_maze(maze_file)
env = MazeEnv(maze_file=maze_file)
obs = env.reset()
prev_time = time()
policy, V, num_iter, total_access = value_iteration(env,
discount_factor=df)
tot_time = time() - prev_time
value_time.append(tot_time)
value_iter.append(num_iter)
value_tot.append(total_access)
coord, action = np.where(policy == 1)
policy = action.reshape(maze_shape)
V = V.reshape(maze_shape)