def play(map_index):
map_npy = 'mappe_test/map_' + map_index + '.npy'
plt.grid(True)
maze = np.load(map_npy)
exit_cell = (30, 5) #(37_27) 80_1 (30,5) 80_2 ....
model_name = 'NN double augm prior 8 rays + delta location ' + map_index
while True:
plt.imshow(maze, cmap="binary")
plt.plot(exit_cell[0], exit_cell[1], "gs",
markersize=5) # exit is a big green square
plt.title(map_npy)
plt.show()
start_cell = tuple(int(x) for x in input('start cell: ').split(
)) #(20,28) (20,25) (14,5) (22,21) 80_1// (38,16) 80_2
game = Maze(maze,
start_cell=start_cell,
exit_cell=exit_cell,
close_reward=-0.5)
model = QReplayDoubleAugmPrior8(game, name=model_name, load=True)
status, trajectory, time_elapsed = game.play(model,
start_cell=start_cell)
game.render("moves")
game.play(model, start_cell=start_cell)
print('*******************************************')
print('status = {}'.format(status))
print('trajectory = {}'.format(trajectory))
print('time elapsed = {} seconds'.format(time_elapsed))
repeat = input('Type True to repeat: ')
if repeat != "True":
break
from models import *
logging.basicConfig(level=logging.INFO,
format="%(levelname)s: %(asctime)s: %(message)s",
datefmt="%H:%M:%S")
maze = np.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 1, 1, 1],
[0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0,
0]]) # 0 = free, 1 = occupied
game = Maze(maze)
if 0: # only show the maze
game.render("moves")
game.reset()
if 0: # play using random model
model = RandomModel(game)
model.train()
if 0: # train using tabular Q-learning
model = QTableModel(game, name="QTableModel")
h, w, _, _ = model.train(discount=0.90,
exploration_rate=0.10,
learning_rate=0.10,
episodes=200)
if 0: # train using tabular Q-learning and an eligibility trace (aka TD-lamba)
model = QTableTraceModel(game)
h # force a NameError exception if h does not exist (and thus don't try to show win rate and cumulative reward)
fig, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True)
fig.canvas.set_window_title(model.name)
ax1.plot(*zip(*w))
ax1.set_xlabel("episode")
ax1.set_ylabel("win rate")
ax2.plot(h)
ax2.set_xlabel("episode")
ax2.set_ylabel("cumulative reward")
plt.show()
except NameError:
pass
plt.grid(True)
plt.imshow(maze, cmap="binary")
plt.show()
game.render("moves")
game.play(model, start_cell = start_cell)
#load = False
actions_counter, close_counter, time, lost = game.win_all_final(model)
plt.savefig('risultato.png')
logging.info('Mean length of path {}, time: {}, with modality {} | lost {}'.format(actions_counter, time, model.name, lost))
logging.info('# of close-to-obstacles states visited in all games: {} with modality {}'.format(close_counter, model.name))
plt.show() # must be placed here else the image disappears immediately at the end of the program
