def CollectingTrainingData(self):
keys = Keys()
screen = Screen()
agent = Agent()
print('Starting Training in...')
# Countdown to start the training
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
paused = False
last_time = time.time()
while True:
if not paused:
grabbed_screen = screen.GrabScreen() # Get actual frame
keys_pressed = keys.KeyCheck() # Check for pressed keys
output_action = keys.KeysActionFreeKicksOutput(
keys_pressed) # Verifies if one action key was pressed
if output_action != [0, 0, 0, 0]:
self.training_data.append(
[grabbed_screen,
output_action]) # Create an instance of training data
if output_action == [1, 0, 0, 0]:
print('Left')
agent.left()
elif output_action == [0, 1, 0, 0]:
print('Right')
agent.right()
elif output_action == [0, 0, 1, 0]:
print('Low Kick')
agent.low_shoot()
elif output_action == [0, 0, 0, 1]:
print('High Kick')
agent.high_shoot()
if len(self.training_data) % 100 == 0 and len(
self.training_data) > 0:
print(len(self.training_data))
keys_pressed = keys.KeyCheck()
# Pausing or Unpausing training
if 'Q' in keys_pressed:
if paused:
paused = False
print('Unpausing training...')
time.sleep(2)
else:
print('Pausing training!')
paused = True
time.sleep(1)
# Saving Data
if 'R' in keys_pressed:
np.save(self.path_file, self.training_data)
class FifaEnv():
def __init__(self):
self.screen = Screen()
self.agent = Agent()
self.score = 0
def check_bug(self):
new_screen = self.screen.GrabScreen()[40:850, 0:1400]
ball_location = new_screen[640:680, 700:740]
if np.mean(ball_location[:, :, 0]) < 60:
return True
else:
return False
def check_end_of_episode(self):
new_screen = self.screen.GrabScreen()
epoch_over_screen_1 = new_screen[630:660, 400:475]
epoch_over_screen_2 = new_screen[630:660, 220:290]
resized_screen_1 = cv2.resize(epoch_over_screen_1, (200, 80))
resized_screen_2 = cv2.resize(epoch_over_screen_2, (200, 80))
image_1 = Image.fromarray(resized_screen_1.astype('uint8'), 'RGB')
image_2 = Image.fromarray(resized_screen_2.astype('uint8'), 'RGB')
ocr_result_1 = pt.image_to_string(image_1)
ocr_result_2 = pt.image_to_string(image_2)
if ocr_result_1 == "REPETIR" or ocr_result_2 == "REPETIR":
return True
else:
return False
def execute_action(self, action):
game_step_over = False
if action == 0:
#print('Action: Move to the left')
self.agent.left()
elif action == 1:
#print('Action: Move to the right')
self.agent.right()
elif action == 2:
#print('Action: Execute a low shoot')
self.agent.low_shoot()
else:
#print('Action: Execute a high shoot')
self.agent.high_shoot()
if action in [2, 3]:
game_step_over = True
time.sleep(3)
else:
time.sleep(1)
return game_step_over
def get_reward(self, screenshot, executed_action, game_step_over):
time.sleep(1)
reward_screen = screenshot[90:140, 1270:1330]
resized_screen = cv2.resize(reward_screen, (800, 300))
image = Image.fromarray(resized_screen.astype('uint8'), 'RGB')
ocr_result = pt.image_to_string(image)
new_score = 0
if ocr_result:
ocr_reward = (''.join(c for c in ocr_result
if c.isdigit() and c != ' ' and c != ''))
if ocr_reward:
new_score = int(ocr_reward)
else:
new_score = self.score
reward = -0.05
## Original
if executed_action in [0, 1]:
# Hasn't shooted the ball yet (Movement actions)
reward = -0.05
#elif np.abs(new_score - self.score) > 500: # without gk
elif np.abs(new_score - self.score) > 500: # with gk
# Scored a goal
reward = 1
elif game_step_over:
# Shooted the ball but didn't score
reward = -1
# Modified
'''if executed_action in [0,1]:
# Hasn't shooted the ball yet (Movement actions)
reward = -0.05
elif np.abs(new_score - self.score) > 1000:
# Scored a goal hitting a target
reward = 1
elif np.abs(new_score - self.score) > 200:
# Scored a normal goal
reward = 0.7
elif np.abs(new_score - self.score) == 200:
# Hit a post
reward = 0.3
elif game_step_over:
# Shooted the ball but didn't score
reward = -1'''
self.score = new_score
return reward
def observe_state(self):
return self.screen.GrabScreen()
def step(self, action):
# Execute an action
game_step_over = self.execute_action(action)
# Observe the new state
new_state = self.observe_state()
# Observe the reward
reward = self.get_reward(new_state, action, game_step_over)
return new_state[40:850, 0:1400], reward, game_step_over
def reset(self):
self.score = 0
self.agent.enter()
def hard_reset(self):
self.score = 0
self.agent.escape()
time.sleep(2)
self.agent.enter()
