sess.run(tf.initialize_all_variables())
summary_writer = tf.train.SummaryWriter(args.save_folder, sess.graph_def)
if args.restore != "No":
saver.restore(sess, args.save_folder+args.restore)
#just display games
sim.reset()
previous_state = numpy.zeros((sim.image_size,sim.image_size,3))
previous_state[:,:,0] = numpy.reshape(sim.screen,(sim.image_size,sim.image_size))
screen = sim.screen
for i in range(1000):
cv2.imshow('Phong!',cv2.resize(screen,(0,0),fx=2,fy=2))
key = cv2.waitKey(8)
if key == UP_KEY:
screen,score,points_made,end = sim.do_action(1,side="left")
elif key == DOWN_KEY:
screen,score,points_made,end = sim.do_action(2,side="left")
else:
screen,score,points_made,end = sim.do_action(0,side="left")
action = learner.return_action(previous_state)
screen,score,points_made,end = sim.do_action(action)
previous_state[:,:,1:] = numpy.copy(previous_state[:,:,:2])
previous_state[:,:,0] = numpy.reshape(screen,(sim.image_size,sim.image_size))
previous_state_image = numpy.copy(previous_state)
previous_state_image -= previous_state_image.min()
previous_state_image /= previous_state_image.max()
previous_state_image *= 255
cv2.imwrite('phong_state.png',previous_state_image)
print(score)
#just display games
sim.reset()
previous_state = numpy.zeros((sim.image_size, sim.image_size, 3))
previous_state[:, :,
0] = numpy.reshape(sim.screen,
(sim.image_size, sim.image_size))
screen = sim.screen
for i in range(1000):
cv2.imshow('Phong!', cv2.resize(screen, (0, 0), fx=2, fy=2))
key = cv2.waitKey(8)
if key == UP_KEY:
screen, score, points_made, end = sim.do_action(
1, side="left")
elif key == DOWN_KEY:
screen, score, points_made, end = sim.do_action(
2, side="left")
else:
screen, score, points_made, end = sim.do_action(
0, side="left")
action = learner.return_action(previous_state)
screen, score, points_made, end = sim.do_action(action)
previous_state[:, :, 1:] = numpy.copy(previous_state[:, :, :2])
previous_state[:, :, 0] = numpy.reshape(
screen, (sim.image_size, sim.image_size))
previous_state_image = numpy.copy(previous_state)
previous_state_image -= previous_state_image.min()
previous_state_image /= previous_state_image.max()
previous_state_image *= 255
cv2.imwrite('phong_state.png', previous_state_image)
print(score)
