class SerpentPikaBallGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.previous_game_frame = None
self.lowerY = np.array([255, 255, 0], np.uint8)
self.upperY = np.array([255, 255, 10], np.uint8)
self.lowerR = np.array([255, 0, 0], np.uint8)
self.upperR = np.array([255, 0, 10], np.uint8)
self.game_state = None
self._reset_game_state()
def setup_key(self):
self.input_mapping = {
"JUMP": [KeyboardKey.KEY_UP],
"RIGHT": [KeyboardKey.KEY_RIGHT],
"LEFT": [KeyboardKey.KEY_LEFT],
"UP_HIT": [KeyboardKey.KEY_UP, KeyboardKey.KEY_RETURN],
"L_HIT": [KeyboardKey.KEY_LEFT, KeyboardKey.KEY_RETURN],
"DOWN_HIT": [KeyboardKey.KEY_DOWN, KeyboardKey.KEY_RETURN],
"NONE": []
}
self.key_mapping = {
KeyboardKey.KEY_UP: "UP",
KeyboardKey.KEY_RIGHT: "RIGHT",
KeyboardKey.KEY_DOWN: "DOWN",
KeyboardKey.KEY_LEFT: "LEFT",
KeyboardKey.KEY_RETURN: "HIT"
}
self.action_space = KeyboardMouseActionSpace(action=[
'JUMP', 'RIGHT', 'LEFT', 'UP_HIT', 'L_HIT', 'DOWN_HIT', 'NONE'
])
move_inputs = {
"JUMP": [KeyboardKey.KEY_UP],
"RIGHT": [KeyboardKey.KEY_RIGHT],
"LEFT": [KeyboardKey.KEY_LEFT],
"NO_MOVE": []
}
attack_inputs = {"Power Hit": [KeyboardKey.KEY_RETURN], "NO_HIT": []}
self.game_inputs = dict()
for move_label, attack_label in itertools.product(
move_inputs, attack_inputs):
label = f"{move_label.ljust(10)}{attack_label}"
self.game_inputs[
label] = move_inputs[move_label] + attack_inputs[attack_label]
print(self.game_inputs)
def setup_play(self):
#self.cid = 0
self.trainID = 0
self.setup_key()
self.frame_process = False
self.rewards = list()
self.started_at = datetime.now()
self.started_at_str = self.started_at.isoformat()
latest_epsilon = 1
model_file_path = 'fighting_movement_dqn_0_1_.h5'
model_list = os.listdir('model')
for item in model_list:
for epsilon in re.findall("\d+\.\d+", item):
if latest_epsilon > float(epsilon):
latest_epsilon = float(epsilon)
model_file_path = item
model_file_path = f'model/{model_file_path}'.replace('/', os.sep)
print(">> LOAD MODEL: ", model_file_path)
time.sleep(1)
self.dqn_action = DDQN(
model_file_path=model_file_path
if os.path.isfile(model_file_path) else None,
input_shape=(114, 162, 4),
input_mapping=self.input_mapping,
action_space=self.action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=100 if os.path.isfile(model_file_path) else 1000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=True)
print('Starting Game')
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
def getDifference(self, game_frame, previous_game_frame):
return game_frame.grayscale_frame - previous_game_frame.grayscale_frame
# old img process way
def handle_frame_process(self, game_frame):
'''
if not self.frame_process:
return
if self.game_frame_buffer.previous_game_frame is not None:
try:
threshold = skimage.filters.threshold_otsu(game_frame.grayscale_frame)
except ValueError:
threshold = -1
gray_frame = game_frame.grayscale_frame > threshold
gray_frame = skimage.filters.gaussian(gray_frame)
gray_cv_frame = cv2.cvtColor(np.array(gray_frame * 255, dtype='uint8'), cv2.COLOR_GRAY2RGB)
cv_frame = cv2.cvtColor(np.asarray(game_frame.frame, dtype='uint8'), cv2.COLOR_BGR2RGB)
cv_frame = cv2.cvtColor(cv_frame, cv2.COLOR_BGR2RGB)
frame_threshed = cv2.inRange(cv_frame, self.lowerY, self.upperY)
# find connected components (pikachu)
_, cnts, hierarchy, = cv2.findContours(frame_threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(cnts) > 0:
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)
for i in range(0, 2):
# Draw a rectangular frame around the detected object
x, y, w, h = cv2.boundingRect(cnts[i])
cv2.rectangle(gray_cv_frame, (x,y), (x+w,y+h), (0,255,0), 2)
frame_threshed = cv2.inRange(cv_frame, self.lowerR, self.upperR)
# find connected components (ball)
_, cnts, hierarchy, = cv2.findContours(frame_threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(cnts) > 0:
cnt = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
if len(cnt) > 70:
(x,y), radius = cv2.minEnclosingCircle(cnt)
cv2.circle(gray_cv_frame, (int(x),int(y)), int(radius), (255,0,0),2)
self.visual_debugger.store_image_data(
gray_cv_frame,
gray_cv_frame.shape,
"grayscale"
)
# send difference to debugger (optional)
frame_difference = self.getDifference(game_frame, self.game_frame_buffer.previous_game_frame)
bw_frame_difference = frame_difference > 100
bw_frame_difference = skimage.filters.sobel(bw_frame_difference)
self.visual_debugger.store_image_data(
np.array(bw_frame_difference * 255, dtype='uint8'),
bw_frame_difference.shape,
"frame_diff"
)
'''
def handle_play(self, game_frame):
# locate sprite position and existence
'''
logo_locator = sprite_locator.locate(sprite=self.game.sprites['SPRITE_LOGO'], game_frame=game_frame)
menu_locator = sprite_locator.locate(sprite=self.game.sprites['SPRITE_MENU'], game_frame=game_frame)
game_set_locator = sprite_locator.locate(sprite=self.game.sprites['SPRITE_GAME_SET'], game_frame=game_frame)
'''
# append memory data into game state
(self.game_state["com_x"], self.game_state["com_y"], self.ai_x,
self.ai_y, self.ball_x, self.ball_y, self.com_sc, self.ai_sc,
self.col_size, self.game_state["col_x"],
self.game_state["col_y"]) = readInfo()
self.game_state["ai_x"].appendleft(self.ai_x)
self.game_state["ai_y"].appendleft(self.ai_y)
self.game_state["ball_x"].appendleft(self.ball_x)
self.game_state["ball_y"].appendleft(self.ball_y)
self.game_state["ai_score"].appendleft(self.ai_sc)
self.game_state["com_score"].appendleft(self.com_sc)
self.game_state["col_size"].appendleft(self.col_size)
self.handle_frame_process(game_frame)
'''
if(logo_locator):
print('Entering Logo...')
self.game_state["playing"] = False
self.handle_menu()
elif (menu_locator):
print('Entering Menu...')
self.game_state["playing"] = False
self.handle_menu()
elif (game_set_locator):
print('Game Set!')
self.handle_fight_end(game_frame)
'''
# judge is-in-game by read pixel value (tricky)
self.game_frame_img = FrameGrabber.get_frames(
[0], frame_type="PIPELINE").frames[0].frame
if self.game_frame_img[100, 81] != 0.7137254901960784:
self.handle_notInGame()
else:
self.game_state["playing"] = True
self.handle_fight(game_frame)
def handle_notInGame(self):
serpent.utilities.clear_terminal()
print('Currently not in game...please wait..')
playAnimation(self.game_state["animeIndex"])
self.game_state["animeIndex"] = self.game_state[
"animeIndex"] + 1 if self.game_state["animeIndex"] < 3 else 0
#print(self.game_frame_img[95:105,80:83])
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
time.sleep(0.3)
def handle_menu(self):
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
time.sleep(4)
def handle_fight(self, game_frame):
gc.disable()
if self.dqn_action.first_run:
self.dqn_action.first_run = False
return
if self.dqn_action.frame_stack is None:
game_frame_buffer = FrameGrabber.get_frames(
[0], frame_type="PIPELINE").frames[0]
self.dqn_action.build_frame_stack(game_frame_buffer.frame)
else:
# saving frame pic to analyze
#self.cid = self.cid + 1
#game_frame_img = FrameGrabber.get_frames([0], frame_type="PIPELINE").frames[0]
#skimage.io.imsave(f"frame{self.cid}.png", game_frame_img.frame)
game_frame_buffer = FrameGrabber.get_frames([0, 1, 2, 3],
frame_type="PIPELINE")
if self.dqn_action.mode == "TRAIN":
reward = self._calculate_reward()
self.game_state["reward"] += reward
self.dqn_action.append_to_replay_memory(
game_frame_buffer,
reward,
terminal=self.game_state["ai_score"][0] == 15)
# Every 2000 steps, save latest weights to disk
if self.dqn_action.current_step % 1000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"model/fighting_movement")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_action.current_step % 10000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"model/fighting_movement",
is_checkpoint=True)
elif self.dqn_action.mode == "RUN":
self.dqn_action.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print('')
print(Fore.YELLOW)
print(Style.BRIGHT)
print(f"STARTED AT:{self.started_at_str}")
print(
f"RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} s"
)
print(Style.RESET_ALL)
#print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print("MOVEMENT NEURAL NETWORK:\n")
self.dqn_action.output_step_data()
print(Style.RESET_ALL)
print(Style.BRIGHT)
print(f"CURRENT RUN: {self.game_state['current_run'] }")
print("")
print(
f"CURRENT RUN REWARD: {round(self.game_state['reward'], 4)}")
print(f"CURRENT AI SCORE: {self.game_state['ai_score'][0]}")
print(f"CURRENT ENEMY SCORE: {self.game_state['com_score'][0]}")
print("")
print(
f"PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(Style.RESET_ALL)
self.dqn_action.pick_action()
self.dqn_action.generate_action()
movement_keys = self.dqn_action.get_input_values()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
#print(movement_keys)
print("" + " + ".join(
list(map(lambda k: self.key_mapping.get(k), movement_keys))))
print(Style.RESET_ALL)
print("")
print(
f"AI: ({self.game_state['ai_x'][0]}, {self.game_state['ai_y'][0]})"
)
print(
f"COM: ({self.game_state['com_x']}, {self.game_state['com_y']})"
)
print(
f"BALL: ({self.game_state['ball_x'][0]}, {self.game_state['ball_y'][0]})"
)
print(
f"Collision: ({self.game_state['col_x']}, {self.game_state['col_y']}, {self.game_state['col_size'][0]})"
)
print(f"Distance: {self.game_state['distance'][0]}")
self.input_controller.handle_keys(movement_keys)
if self.dqn_action.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_action.erode_epsilon(factor=2)
self.dqn_action.next_step()
self.game_state["current_run"] += 1
if self.game_state['ai_score'][0] == 15 or self.game_state[
'com_score'][0] == 15:
# Game over
self.game_state["ai_score"].appendleft(0)
self.game_state["com_score"].appendleft(0)
self.handle_fight_end(game_frame)
def handle_fight_end(self, game_frame):
self.game_state["playing"] = False
self.input_controller.handle_keys([])
self.game_state["current_run"] += 1
self.handle_fight_training(game_frame)
def handle_fight_training(self, game_frame):
#self.input_controller.tap_key(KeyboardKey.KEY_ESCAPE)
serpent.utilities.clear_terminal()
gc.enable()
gc.collect()
gc.disable()
print("TRAIN MODE")
self.input_controller.handle_keys([])
if self.dqn_action.mode == "TRAIN":
for i in range(16):
serpent.utilities.clear_terminal()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print(f"TRAINING ON MINI-BATCHES: {i + 1}/16")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 25 == 0 else ''}"
)
print(Style.RESET_ALL)
self.dqn_action.train_on_mini_batch()
self.game_state["run_predicted_actions"] = 0
if self.dqn_action.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
self.dqn_action.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_action.enter_run_mode()
else:
self.dqn_action.enter_train_mode()
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
time.sleep(2)
def _reset_game_state(self):
self.game_state = {
"reward": 0,
"animeIndex": 0,
"current_run": 1,
"playing": False,
"run_predicted_actions": 0,
"ai_x": collections.deque(np.full((4, ), 0), maxlen=4),
"ai_y": collections.deque(np.full((4, ), 0), maxlen=4),
"ai_score": collections.deque(np.full((4, ), 0), maxlen=4),
"ball_x": collections.deque(np.full((4, ), 0), maxlen=4),
"ball_y": collections.deque(np.full((4, ), 0), maxlen=4),
"com_score": collections.deque(np.full((4, ), 0), maxlen=4),
"col_size": collections.deque(np.full((4, ), 6), maxlen=4),
"com_x": 36,
"com_y": 244,
"col_x": 0,
"col_y": 0,
"distance": collections.deque(np.full((20, ), 100), maxlen=20),
}
def _calculate_reward(self):
reward = 0
distance = math.sqrt(
abs(self.game_state["ai_x"][0] - self.game_state["ball_x"][0])**2 +
abs(self.game_state["ai_y"][0] - self.game_state["ball_y"][0])**2)
self.game_state["distance"].appendleft(int(distance))
# collision with ball
collision = self.game_state["distance"][0] < 80 and self.game_state[
"distance"][1] < 80 and self.game_state["distance"][
2] < 80 and self.game_state["distance"][0] > self.game_state[
"distance"][1] and self.game_state["distance"][
1] < self.game_state["distance"][2]
if collision:
reward += 0.25
# power hit
if self.game_state["col_size"][0] > 0 and self.game_state["distance"][
0] < 90 and self.game_state["col_y"] != 272:
reward += 0.5
# AI gain score
if self.game_state["ai_score"][0] > self.game_state["ai_score"][1]:
reward += 1
# Com gain score
if self.game_state["com_score"][0] > self.game_state["com_score"][1]:
reward += -1
if reward > 1:
reward = 1
self.game_state["reward"] = reward
return reward
class SerpentFortniteGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.game_state = None
self._reset_game_state()
def setup_play(self):
self.detector = ObjectDetection()
self.detector.setModelTypeAsTinyYOLOv3()
self.detector.setModelPath("yolo.h5")
self.detector.loadModel(detection_speed="flash")
input_mapping = {
"KEY_W": [KeyboardKey.KEY_W],
"KEY_A": [KeyboardKey.KEY_A],
"KEY_S": [KeyboardKey.KEY_S],
"KEY_D": [KeyboardKey.KEY_D],
"KEY_SPACE": [KeyboardKey.KEY_SPACE],
"KEY_C": [KeyboardKey.KEY_C],
"KEY_1": [KeyboardKey.KEY_1],
"KEY_2": [KeyboardKey.KEY_2]
}
self.key_mapping = {
KeyboardKey.KEY_W.name: "KEY_W",
KeyboardKey.KEY_A.name: "KEY_A",
KeyboardKey.KEY_S.name: "KEY_S",
KeyboardKey.KEY_D.name: "KEY_D",
KeyboardKey.KEY_SPACE.name: "KEY_SPACE",
KeyboardKey.KEY_C.name: "KEY_C",
KeyboardKey.KEY_1.name: "KEY_1",
KeyboardKey.KEY_2.name: "KEY_2"
}
direction_action_space = KeyboardMouseActionSpace(direction_keys=[
"KEY_W", "KEY_A", "KEY_S", "KEY_D", "KEY_SPACE", "KEY_C", "KEY_1",
"KEY_2"
])
direction_model_file_path = "datasets/Fortnite_direction_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_direction = DDQN(
model_file_path=direction_model_file_path
if os.path.isfile(direction_model_file_path) else None,
input_shape=(480, 640, 4),
input_mapping=input_mapping,
action_space=direction_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=600,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=1,
final_epsilon=0.01,
)
def handle_play(self, game_frame):
gc.disable()
for i, game_frame in enumerate(self.game_frame_buffer.frames):
self.visual_debugger.store_image_data(game_frame.frame,
game_frame.frame.shape,
str(i))
if self.dqn_direction.first_run:
# self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
# time.sleep(5)
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
self.dqn_direction.first_run = False
return None
actor_hp = self._measure_actor_hp(game_frame)
run_score = self._measure_run_score(game_frame)
self.game_state["health"].appendleft(actor_hp)
self.game_state["score"].appendleft(run_score)
if self.dqn_direction.frame_stack is None:
full_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE").frames[0]
self.dqn_direction.build_frame_stack(full_game_frame.frame)
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE")
if self.dqn_direction.mode == "TRAIN":
reward_direction, reward_action = self._calculate_reward()
self.game_state["run_reward_direction"] += reward_direction
self.game_state["run_reward_action"] += reward_action
self.dqn_direction.append_to_replay_memory(
game_frame_buffer,
reward_direction,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_direction.current_step % 2000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/Fortnite_direction")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_direction.current_step % 20000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/Fortnite_direction",
is_checkpoint=True)
elif self.dqn_direction.mode == "RUN":
self.dqn_direction.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes,, {run_time.seconds % 60} seconds"
)
print(
"GAME: Fortnite PLATFORM: EXE AGENT: DDQN + Prioritized Experience Replay"
)
print("")
self.dqn_direction.output_step_data()
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_direction'] + self.game_state['run_reward_action'], 2)}"
)
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(f"CURRENT SCORE: {self.game_state['score'][0]}")
print("")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
if self.game_state["health"][1] <= 0:
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_direction.mode == "RUN"
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
if self.dqn_direction.mode == "TRAIN":
for i in range(8):
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(
"GAME: Fortnite PLATFORM: EXE AGENT: DDQN + Prioritized Experience Replay"
)
print("")
print(f"TRAINING ON MINI-BATCHES: {i + 1}/2")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}"
)
self.dqn_direction.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_direction"] = 0
self.game_state["run_reward_action"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ),
3),
maxlen=8)
self.game_state["score"] = collections.deque(np.full((8, ), 0),
maxlen=8)
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
if self.dqn_direction.type == "DDQN":
self.dqn_direction.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_direction.enter_run_mode()
else:
self.dqn_direction.enter_train_mode()
# self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
# time.sleep(3)
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
return None
self.dqn_direction.pick_action()
self.dqn_direction.generate_action()
keys = self.dqn_direction.get_input_values()
print("")
print(keys)
img = pyautogui.screenshot(region=(0, 0, 1920, 1080))
# convert image to numpy array
im = np.array(img)
custom = self.detector.CustomObjects(person=True)
detections = self.detector.detectCustomObjectsFromImage(
custom_objects=custom, input_type="array", input_image=im)
for eachObject in detections:
print(eachObject["box_points"])
tuple_of_x_and_y = eachObject["box_points"]
centerX = (tuple_of_x_and_y[0] + tuple_of_x_and_y[2]) / 2
centerY = (tuple_of_x_and_y[1] + tuple_of_x_and_y[3]) / 2
centerX = int(centerX)
centerY = int(centerY)
ctypes.windll.user32.SetCursorPos(centerX, centerY)
ctypes.windll.user32.mouse_event(2, 0, 0, 0, 0) # left down
time.sleep(0.05)
ctypes.windll.user32.mouse_event(4, 0, 0, 0, 0) # left up
self.shot_reward = 100000
self.input_controller.handle_keys(keys)
if self.dqn_direction.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_direction.erode_epsilon(factor=2)
self.dqn_direction.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 3), maxlen=8),
"score": collections.deque(np.full((8, ), 0), maxlen=8),
"run_reward_direction": 0,
"run_reward_action": 0,
"current_run": 1,
"current_run_steps": 0,
"current_run_health": 0,
"current_run_score": 0,
"run_predicted_actions": 0,
"last_run_duration": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"run_timestamp": datetime.utcnow(),
}
def _measure_actor_hp(self, game_frame):
hp_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["HP_AREA"])
hp_area_image = Image.fromarray(hp_area_frame)
actor_hp = 0
image_colors = hp_area_image.getcolors(
) # TODO: remove in favor of sprite detection and location
if image_colors:
actor_hp = len(image_colors) - 7
for name, sprite in self.game.sprites.items():
sprite_to_locate = Sprite("QUERY", image_data=sprite.image_data)
sprite_locator = SpriteLocator()
location = sprite_locator.locate(sprite=sprite_to_locate,
game_frame=game_frame)
print(location)
if location:
actor_hp = 1000000
return actor_hp
def _measure_run_score(self, game_frame):
score_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["SCORE_AREA"])
score_grayscale = np.array(skimage.color.rgb2gray(score_area_frame) *
255,
dtype="uint8")
score_image = Image.fromarray(score_grayscale)
score = '0'
image_colors = score_image.getcolors()
if image_colors and len(image_colors) > 1:
score = serpent.ocr.perform_ocr(image=score_grayscale,
scale=10,
order=5,
horizontal_closing=10,
vertical_closing=5)
score = score.split(":")[0]
count = 0
if not score.isdigit():
score = '0'
self.game_state["current_run_score"] = score
return score
def _calculate_reward(self):
reward = 0
reward = self.shot_reward
reward += self.game_state["health"][0] / 10.0
return reward, reward
class SerpentCloneyGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# self.frame_handlers["PLAY"] = self.handle_play
self.frame_handlers["PLAY_DDQN"] = self.handle_play_ddqn
# self.frame_handler_setups["PLAY"] = self.setup_play
self.frame_handler_setups["PLAY_DDQN"] = self.setup_play_ddqn
self.analytics_client = None
def setup_play(self):
self.plugin_path = offshoot.config["file_paths"]["plugins"]
# Context Classifier
context_classifier_path = f"{self.plugin_path}/SerpentCloneyGameAgentPlugin/files/ml_models/cloney_context_classifier.model"
context_classifier = CNNInceptionV3ContextClassifier(input_shape=(288, 512, 3))
context_classifier.prepare_generators()
context_classifier.load_classifier(context_classifier_path)
self.machine_learning_models["context_classifier"] = context_classifier
# Object Detection of leaves
self.object_detector = ObjectDetector(graph_fp=f'{self.plugin_path}/SerpentCloneyGameAgentPlugin/files/ml_models/cloney_detection/frozen_inference_graph.pb',
labels_fp=f'{self.plugin_path}/SerpentCloneyGameAgentPlugin/files/ml_models/cloney_detection/cloney-detection.pbtxt',
num_classes=2,
threshold=0.6)
# Reset Variables
self._reset_game_state()
# =============================
# -----------DQN TODO ---------
# =============================
def setup_play_ddqn(self):
self._reset_game_state()
input_mapping = {
"UP": [KeyboardKey.KEY_SPACE]
}
self.key_mapping = {
KeyboardKey.KEY_SPACE.name: "UP"
}
movement_action_space = KeyboardMouseActionSpace(
default_keys=[None, "UP"]
)
movement_model_file_path = "datasets/cloney_direction_dqn_0_1_.hp5".replace("/", os.sep)
self.dqn_movement = DDQN(
model_file_path=movement_model_file_path if os.path.isfile(movement_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=movement_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=1000,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False
)
def handle_play_ddqn(self, game_frame):
gc.disable()
if self.dqn_movement.first_run:
self.input_controller.tap_key(KeyboardKey.KEY_W)
self.dqn_movement.first_run = False
time.sleep(5)
return None
dragon_alive = self._measure_dragon_alive(game_frame)
# dragon_coins = self._measure_dragon_coins(game_frame)
self.game_state["alive"].appendleft(dragon_alive)
# self.game_state["coins"].appendleft(dragon_coins)
if self.dqn_movement.frame_stack is None:
# pipeline_game_frame = FrameGrabber.get_frames(
# [0],
# frame_shape=game_frame.frame.shape,
# frame_type="MINI"
# ).frames[0]
self.dqn_movement.build_frame_stack(game_frame.ssim_frame)
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=game_frame.frame.shape,
frame_type="MINI"
)
if self.dqn_movement.mode == "TRAIN":
reward = self._calculate_reward()
self.game_state["run_reward"] += reward
self.dqn_movement.append_to_replay_memory(
game_frame_buffer,
reward,
terminal=self.game_state["alive"] == 0
)
# Every 2000 steps, save latest weights to disk
if self.dqn_movement.current_step % 2000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/cloney_movement"
)
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_movement.current_step % 20000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/cloney_movement",
is_checkpoint=True
)
elif self.dqn_movement.mode == "RUN":
self.dqn_movement.update_frame_stack(self.game_frame_buffer)
run_time = datetime.now() - self.started_at
print("\033c" + f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds")
print("")
print("MOVEMENT NEURAL NETWORK:\n")
self.dqn_movement.output_step_data()
print("")
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(f"CURRENT RUN REWARD: {round(self.game_state['run_reward'], 2)}")
print(f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}")
print(f"CURRENT DRAGON ALIVE: {self.game_state['alive'][0]}")
# print(f"CURRENT DRAGON COINS: {self.game_state['coins'][0]})
print("")
# print(f"AVERAGE ACTIONS PER SECOND: {round(self.game_state['average_aps'], 2)}")
print("")
print(f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds")
# print(f"LAST RUN COINS: {self.game_state['last_run_coins'][0]})
print("")
print(f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})")
# print(f"RECORD COINS COLLECTED: {self.game_state['record_coins_collected'].get('value')} coins (Run {self.game_state['record_coins_collected'].get('run')}, {'Predicted' if self.game_state['record_coins_collected'].get('predicted') else 'Training'})")
print("")
print(f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds")
if self.game_state["alive"][1] <= 0:
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
# Set display stuff TODO
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state["record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_movement.mode == "RUN"
}
# if self.game_state["coins"][0] < self.game_state["record_coins_collected"].get("value", 1000):
# self.game_state["record_coins_collected"] = {
# "value": self.game_state["coins"][0],
# "run": self.game_state["current_run"],
# "predicted": self.dqn_movement.mode == "RUN"
# }
else:
self.game_state["random_time_alives"].append(self.game_state["last_run_duration"])
self.game_state["random_time_alive"] = np.mean(self.game_state["random_time_alives"])
self.game_state["current_run_steps"] = 0
self.input_controller.release_key(KeyboardKey.KEY_SPACE)
if self.dqn_movement.mode == "TRAIN":
for i in range(8):
serpent.utilities.clear_terminal()
print(f"TRAINING ON MINI-BATCHES: {i + 1}/8")
print(f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}")
self.dqn_movement.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_movement"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["alive"] = collections.deque(np.full((8,), 4), maxlen=8)
# self.game_state["coins"] = collections.deque(np.full((8,), 0), maxlen=8)
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state["current_run"] % 100 == 0:
if self.dqn_movement.type == "DDQN":
self.dqn_movement.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state["current_run"] % 20 == 0:
self.dqn_movement.enter_run_mode()
else:
self.dqn_movement.enter_train_mode()
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
time.sleep(5)
return None
self.dqn_movement.pick_action()
self.dqn_movement.generate_action()
keys = self.dqn_movement.get_input_values()
print("")
print(" + ".join(list(map(lambda k: self.key_mapping.get(k.name), keys))))
self.input_controller.handle_keys(keys)
if self.dqn_movement.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_movement.erode_epsilon(factor=2)
self.dqn_movement.next_step()
self.game_state["current_run_steps"] += 1
def handle_play(self, game_frame):
context = self.machine_learning_models["context_classifier"].predict(game_frame.frame)
if context is None:
return
if context == "GAME_WORLD_1":
self.display_game_agent_state(context=context)
self.handle_play_context_game_world(game_frame=game_frame)
self.in_progress_game_over = False
elif context == "GAME_OVER":
self.display_game_agent_state(context=context)
time.sleep(2)
if self.in_progress_game_over is False:
self.handle_play_context_game_over(game_frame=game_frame)
elif context == "MAIN_MENU":
self.input_controller.click_screen_region(screen_region="MAIN_MENU_PLAY")
time.sleep(3.5)
self.current_run_started_at = datetime.utcnow()
elif context == "GAME_PAUSE":
self.handle_play_context_game_pause(game_frame)
def handle_play_context_game_world(self, game_frame):
# Only predict if object_detector is idle
if self.object_detector.get_status() is False:
self.object_predictions = self.object_detector.predict(frame=game_frame.frame)
for prediction in self.object_predictions:
if prediction['class'] == "dragon":
self.positions['dragon_pos_right_x'] = prediction['bb_o'][3]
self.positions['dragon_pos_left_x'] = prediction['bb_o'][1]
self.positions['dragon_pos_mid_y'] = (prediction['bb_o'][0] + prediction['bb_o'][2]) / 2
self.positions['dragon_pos_mid_x'] = (prediction['bb_o'][1] + prediction['bb_o'][3]) / 2
self.dragon_object = prediction
elif prediction['class'] == "leaves":
self.positions['leaf_pos_mid_y'] = (prediction['bb_o'][0] + prediction['bb_o'][2]) / 2
self.positions['leaf_pos_top_y'] = prediction['bb_o'][0]
self.positions['leaf_pos_bottom_y'] = prediction['bb_o'][2]
self.positions['leaf_pos_right_x'] = prediction['bb_o'][3]
self.positions['leaf_pos_left_x']= prediction['bb_o'][1]
self.leaf_object = prediction
if (self.positions['dragon_pos_mid_y'] > (self.positions['leaf_pos_top_y'] - 50) and self.positions['dragon_pos_mid_y'] < (self.positions['leaf_pos_bottom_y']) + 50) and (self.positions['dragon_pos_right_x'] + 100) > self.positions['leaf_pos_left_x']: # Same height
self.warning = "HIGH"
if self.positions['dragon_pos_right_x'] + 100 > self.positions['leaf_pos_left_x']:
self.input_controller.tap_key(KeyboardKey.KEY_S, duration=0.025)
time.sleep(0.1)
elif self.positions['dragon_pos_mid_y'] - 50 < self.positions['leaf_pos_bottom_y']:
time.sleep(0.225)
self.input_controller.tap_key(KeyboardKey.KEY_S, duration=0.025)
elif self.positions['dragon_pos_mid_y'] + 50 > self.positions['leaf_pos_top_y']:
self.input_controller.tap_key(KeyboardKey.KEY_S, duration=0.025)
time.sleep(0.1)
break
else:
self.warning = "SAFE"
self.input_controller.tap_key(KeyboardKey.KEY_S, duration=0.026)
time.sleep(0.23)
break
def handle_play_context_game_over(self, game_frame):
self.in_progress_game_over = True
time.sleep(4)
self.game_state['last_run_duration'] = (datetime.utcnow() - self.game_state['current_run_started_at']).seconds if self.game_state['current_run_started_at'] else 0
self.game_state['last_run'] = self.game_state['current_run']
if self.game_state['record_duration'] is not None:
if self.game_state['last_run_duration'] > self.game_state['record_duration']:
self.game_state['record_duration'] = self.game_state['last_run_duration']
self.game_state['record_run'] = self.game_state['last_run']
else:
self.game_state['record_duration'] = self.game_state['last_run_duration']
# Process Image for OCR
frame = game_frame.frame
gray_frame = skimage.color.rgb2gray(frame)
frame_coins = gray_frame[190:300, 250: 780]
frame_distance = gray_frame[355:410, 550:760]
frame_time = gray_frame[300:355, 550:760]
# Find Coins
text_coins = ocr.perform_ocr(image=frame_coins, scale=2, order=5, horizontal_closing=2, vertical_closing=3)
# Find Distance
text_distance = ocr.perform_ocr(image=frame_distance, scale=2, order=5, horizontal_closing=2, vertical_closing=3)
text_time = ocr.perform_ocr(image=frame_time, scale=2, order=5, horizontal_closing=2, vertical_closing=3)
print(text_coins)
print(text_time)
print(text_distance)
# if "$" in coins:
# num_coins = coins.replace('$', '')
# self.game_state['last_run_coins_collected'] = int(num_coins)
#
# if self.game_state['last_run_coins_collected'] > self.game_state['record_coins_collected']:
# self.game_state['record_coins_collected'] = self.game_state['last_run_coins_collected']
# Find Distance and Time
#candidates, regions = ocr.extract_ocr_candidates(image=frame, gradient_size=3, closing_size=10, minimum_area=100, minimum_aspect_ratio=2)
#print(regions)
#gray_frame = skimage.color.rgb2gray(frame)
# for region in regions:
# crop = gray_frame[region[0]:region[2], region[1]:region[3]]
# read = ocr.perform_ocr(image=crop, scale=1, order=5, horizontal_closing=1, vertical_closing=1)
# print(read)
# if "Distance" in read or "Time":
# self.pos_d = regions.index(region) + 1
# elif "Time" in read:
# self.pos_t = regions.index(region) + 1
#
# if regions.index(region) == self.pos_d:
# self.game_state['last_run_distance'] = read.replace('m', '')
# if self.game_state['last_run_distance'] > self.game_state['record_distance']:
# self.game_state['record_distance'] = self.game_state['last_run_distance']
# elif regions.index(region) == self.pos_t:
# self.game_state['last_run_duration_actual'] = read
# Have to still check for record. Find out about time formatting
# Click PLAY button to start a new run
#self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
self.input_controller.click_screen_region(screen_region="GAME_OVER_PLAY")
# Wait for "Ready, Set, Tap"
time.sleep(3)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(0.2)
self.game_state['current_run'] += 1
self.game_state['current_run_started_at'] = datetime.utcnow()
def handle_play_context_main_menu(self, game_frame):
self.input_controller.click_screen_region(screen_region="MAIN_MENU_PLAY")
def handle_play_context_game_pause(self, game_frame):
time.sleep(1)
self.input_controller.click_screen_region(screen_region="GAME_PAUSE")
def display_game_agent_state(self, context):
self.game_state['current_run_duration'] = (datetime.utcnow() - self.game_state['current_run_started_at']).seconds
print("\033c")
print("======================================================")
print(f"GAME: Cloney PLATFORM: Steam VERSION: v0.0.1")
print("======================================================")
print("")
print(xtermcolor.colorize("OBJECT DETECTION", ansi=9))
print(f"Detected: {len(self.object_predictions)} objects")
if self.warning == "HIGH":
print(xtermcolor.colorize(f"Danger Level: {self.warning}", ansi=1))
elif self.warning == "SAFE":
print(xtermcolor.colorize(f"Danger Level: {self.warning}", ansi=2))
# print(f"DRAGON POS: {self.dragon_object['bb_o'][0]}, {self.dragon_object['bb_o'][1]}, {self.dragon_object['bb_o'][2]}, {self.dragon_object['bb_o'][3]}")
# print(f"LAST LEAF POS: {self.leaf_object['bb_o'][0]}, {self.leaf_object['bb_o'][1]}. {self.leaf_object['bb_o'][2]}. {self.leaf_object['bb_o'][3]}")
print("")
print(xtermcolor.colorize("GAME STATISTICS", ansi=9))
print(f"Current Context: {context}\n")
print(f"Current Run: #{self.game_state['current_run']}")
print(f"Current Run Duration: {self.game_state['current_run_duration']}s")
print("")
print(f"Last Run: #{self.game_state['last_run']}")
print(f"Last Run Duration: {self.game_state['last_run_duration']}s")
print(f"Last Run Duration Actual: {self.game_state['last_run_duration_actual']}")
print(f"Last Run Distance: {self.game_state['last_run_distance']}m")
print(f"Last Run Coins Collected: {self.game_state['last_run_coins_collected']}")
print(f"Record Duration: {self.game_state['record_duration']}s (Run #{self.game_state['record_run']})")
def _reset_game_state(self):
# Display Variables
self.game_state = {
"alive": collections.deque(np.full((8,), 4), maxlen=8),
"coins": collections.deque(np.full((8,), 0), maxlen=8),
"current_run": 1,
"current_run_started_at": datetime.utcnow(),
"current_run_duration": None,
"current_run_steps": 0,
"run_reward": 0,
"run_future_rewards": 0,
"run_predicted_actions": 0,
"run_timestamp": datetime.utcnow(),
"last_run": 0,
"last_run_duration": 0,
"last_run_duration_actual": None,
"last_run_distance": 0.0,
"last_run_coins_collected": 0,
"record_duration": None,
"record_duration_actual": 0,
"record_run": 0,
"record_distance": 0.0,
"record_coins_collected": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"random_distance_travelled": 0.0
}
# Object Detection Variables
self.object_predictions = []
self.warning = ""
self.dragon_object = []
self.leaf_object = []
self.positions = {
'leaf_pos_mid_y': 0,
'leaf_pos_right_x': 0,
'leaf_pos_left_x': 0,
'leaf_pos_top_y': 0,
'leaf_pos_bottom_y': 0,
'dragon_pos_right_x': 0,
'dragon_pos_left_x': 0,
'dragon_pos_mid_y': 0,
'dragon_pos_mid_x': 0
}
# Other Variables
self.pos_d = -1
self.pos_t = -1
self.in_progress_game_over = False
def _measure_dragon_alive(self, game_frame):
dollar_area_frame = serpent.cv.extract_region_from_image(game_frame.frame, self.game.screen_regions["DOLLAR_AREA"])
dragon_alive = None
max_ssim = 0
for name, sprite in self.game.sprites.items():
print(name)
print(name[-1])
for i in range(sprite.image_data.shape[3]):
ssim = skimage.measure.compare_ssim(dollar_area_frame, np.squeeze(sprite.image_data[..., :3, i]), multichannel=True)
if ssim > max_ssim:
max_ssim = ssim
dragon_alive = 1 # int(name[-1])
return dragon_alive
def _measure_dragon_coins(self, game_frame):
coins_area_frame = serpent.cv.extract_region_from_image(game_frame.frame, self.game.screen_regions["COINS_AREA"])
return coins_area_frame[coins_area_frame[..., 2] > 150].size
def _calculate_reward(self):
reward = 0
reward += (-0.5 if self.game_state["alive"][0] < self.game_state["alive"][1] else 0.05)
# reward += (0.5 if (self.game_state["coins"][0] - self.game_state["coins"][1]) >= 1 else -0.05)
return reward
class SerpentRoboGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.sprite_locator = SpriteLocator()
self.game_state = None
self._reset_game_state()
def setup_play(self):
input_mapping = {
"W": [KeyboardKey.KEY_W],
"A": [KeyboardKey.KEY_A],
"S": [KeyboardKey.KEY_S],
"D": [KeyboardKey.KEY_D],
"WA": [KeyboardKey.KEY_W, KeyboardKey.KEY_A],
"WD": [KeyboardKey.KEY_W, KeyboardKey.KEY_D],
"SA": [KeyboardKey.KEY_S, KeyboardKey.KEY_A],
"SD": [KeyboardKey.KEY_S, KeyboardKey.KEY_D],
"J": [KeyboardKey.KEY_J],
"K": [KeyboardKey.KEY_K],
"L": [KeyboardKey.KEY_L],
"U": [KeyboardKey.KEY_U],
"I": [KeyboardKey.KEY_I],
"O": [KeyboardKey.KEY_O],
"JU": [KeyboardKey.KEY_J, KeyboardKey.KEY_U],
"KI": [KeyboardKey.KEY_K, KeyboardKey.KEY_I],
"LO": [KeyboardKey.KEY_L, KeyboardKey.KEY_O],
"N": [KeyboardKey.KEY_N],
"M": [KeyboardKey.KEY_M],
"NONE": []
}
self.key_mapping = {
KeyboardKey.KEY_W.name: "MOVE UP",
KeyboardKey.KEY_A.name: "MOVE LEFT",
KeyboardKey.KEY_S.name: "MOVE DOWN",
KeyboardKey.KEY_D.name: "MOVE RIGHT",
KeyboardKey.KEY_J.name: "LIGHT PUNCH",
KeyboardKey.KEY_K.name: "MEDIUM PUNCH",
KeyboardKey.KEY_L.name: "HARD PUNCH",
KeyboardKey.KEY_U.name: "LIGHT KICK",
KeyboardKey.KEY_I.name: "MEDIUM KICK",
KeyboardKey.KEY_O.name: "HARD KICK",
KeyboardKey.KEY_N.name: "START",
KeyboardKey.KEY_M.name: "SELECT"
}
movement_action_space = KeyboardMouseActionSpace(directional_keys=[
"W", "A", "S", "D", "WA", "WD", "SA", "SD", "NONE"
])
fightinput_action_space = KeyboardMouseActionSpace(fightinput_keys=[
"J", "K", "L", "U", "I", "O", "JU", "KI", "LO", "NONE"
])
movement_model_file_path = "datasets/fighting_movement_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_movement = DDQN(
model_file_path=movement_model_file_path
if os.path.isfile(movement_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=movement_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=1000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False)
fightinput_model_file_path = "datasets/fighting_fightinput_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_fightinput = DDQN(
model_file_path=fightinput_model_file_path
if os.path.isfile(fightinput_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=fightinput_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=1000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False)
print("Debug: Game Started")
def handle_play(self, game_frame):
#print("Debug: Main")
title_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_TITLE_TEXT'],
game_frame=game_frame)
menu_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_MAINMENU_TEXT'],
game_frame=game_frame)
fightmenu_select_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_FIGHTMENU_SELECT'],
game_frame=game_frame)
playerselect_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_PLAYERSELECT'],
game_frame=game_frame)
backbutton_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_BACKBUTTON'],
game_frame=game_frame)
fightcheck_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_FIGHTCHECK'],
game_frame=game_frame)
roundstart_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_ROUNDSTART'],
game_frame=game_frame)
retrybutton_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_FIGHTMENU_RETRY'],
game_frame=game_frame)
backbutton_locator = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_BACKBUTTON'],
game_frame=game_frame)
(self.p1hp, self.p2hp) = readhp()
self.game_state["health"].appendleft(self.p1hp)
self.game_state["enemy_health"].appendleft(self.p2hp)
if (roundstart_locator):
#print("Debug: roundstart_locator Locator")
self.game_state["fightstarted"] = True
elif (retrybutton_locator):
#print("Debug: retrybutton_locator Locator")
self.handle_fight_end(game_frame)
elif (fightcheck_locator):
#print("Debug: fightcheck_locator Locator")
self.handle_fight(game_frame)
elif (title_locator):
#print("Debug: title_locator Locator")
self.handle_menu_title(game_frame)
elif (menu_locator):
#print("Debug: menu_locator Locator")
self.handle_menu_select(game_frame)
elif (playerselect_locator):
#print("Debug: playerselect_locator Locator")
self.handle_player_select(game_frame)
elif (backbutton_locator):
#print("Debug: backbutton_locator Locator")
self.handle_backbutton(game_frame)
elif ((fightmenu_select_locator)
and (self.game_state["current_run"] != 1)):
#print("Debug: fightmenu_select_locator Locator")
self.handle_fightmenu_select(game_frame)
else:
return
def handle_retry_button(self, game_frame):
if (self.game_state["current_run"] % 25 == 0):
print(Fore.RED + 'Changing Opponent')
print(Style.RESET_ALL)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(1)
else:
print(Fore.RED + 'Restarting Fight')
print(Style.RESET_ALL)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(1)
def handle_backbutton(self, game_frame):
print(Fore.RED + 'Pressing Select')
print(Style.RESET_ALL)
self.input_controller.tap_key(KeyboardKey.KEY_M)
time.sleep(1)
def handle_menu_title(self, game_frame):
print(Fore.RED + 'Pressing Start')
print(Style.RESET_ALL)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(2)
def handle_fightmenu_select(self, game_frame):
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(2)
def handle_player_select(self, game_frame):
time.sleep(1)
print(Fore.RED + 'Choosing one Char')
self.input_controller.tap_key(KeyboardKey.KEY_A)
time.sleep(0.3)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(0.5)
print("Choosing Robo")
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(0.3)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(0.3)
self.input_controller.tap_key(KeyboardKey.KEY_D)
time.sleep(0.3)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(0.5)
print("Choosing one CPU Char")
self.input_controller.tap_key(KeyboardKey.KEY_A)
time.sleep(0.3)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(0.3)
print("Choosing Random CPU Char")
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(0.3)
print("Starting Game")
print(Style.RESET_ALL)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(1)
def handle_menu_select(self, game_frame):
menu_selector = sprite_locator.locate(
sprite=self.game.sprites['SPRITE_MAINMENU_SINGLEPLAY'],
game_frame=game_frame)
if (menu_selector):
print(Fore.RED + 'Starting Singleplayer Mode')
print(Style.RESET_ALL)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_J)
time.sleep(1)
else:
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(1)
def handle_fight(self, game_frame):
gc.disable()
if not (self.game_state["fightstarted"]):
return
if ((self.game_state["health"][0] == 0) and
(self.game_state["health"][1] == 0)
or (self.game_state["enemy_health"][0] == 0) and
(self.game_state["enemy_health"][1] == 0)):
return
if self.dqn_movement.first_run:
self.dqn_movement.first_run = False
self.dqn_fightinput.first_run = False
return None
if self.dqn_movement.frame_stack is None:
pipeline_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE",
dtype="float64").frames[0]
self.dqn_movement.build_frame_stack(pipeline_game_frame.frame)
self.dqn_fightinput.frame_stack = self.dqn_movement.frame_stack
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE",
dtype="float64")
if self.dqn_movement.mode == "TRAIN":
reward_movement, reward_fightinput = self._calculate_reward()
self.game_state["run_reward_movement"] += reward_movement
self.game_state["run_reward_fightinput"] += reward_fightinput
self.dqn_movement.append_to_replay_memory(
game_frame_buffer,
reward_movement,
terminal=self.game_state["health"] == 0)
self.dqn_fightinput.append_to_replay_memory(
game_frame_buffer,
reward_fightinput,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_movement.current_step % 2000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/fighting_movement")
self.dqn_fightinput.save_model_weights(
file_path_prefix=f"datasets/fighting_fightinput")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_movement.current_step % 20000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/fighting_movement",
is_checkpoint=True)
self.dqn_fightinput.save_model_weights(
file_path_prefix=f"datasets/fighting_fightinput",
is_checkpoint=True)
elif self.dqn_movement.mode == "RUN":
self.dqn_movement.update_frame_stack(game_frame_buffer)
self.dqn_fightinput.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print("")
print(Fore.YELLOW)
print(Style.BRIGHT)
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(Style.RESET_ALL)
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print("MOVEMENT NEURAL NETWORK:\n")
self.dqn_movement.output_step_data()
print("")
print("FIGHT NEURAL NETWORK:\n")
self.dqn_fightinput.output_step_data()
print(Style.RESET_ALL)
print("")
print(Style.BRIGHT)
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_movement'] + self.game_state['run_reward_fightinput'], 4)}"
)
print(
f"COMBO MULTIPLICATOR: {self.game_state['multiplier_damage']}")
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(
f"CURRENT ENEMY HEALTH: {self.game_state['enemy_health'][0]}")
print("")
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(Style.RESET_ALL)
self.dqn_movement.pick_action()
self.dqn_movement.generate_action()
self.dqn_fightinput.pick_action(
action_type=self.dqn_movement.current_action_type)
self.dqn_fightinput.generate_action()
movement_keys = self.dqn_movement.get_input_values()
fightinput_keys = self.dqn_fightinput.get_input_values()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print("" + " + ".join(
list(
map(lambda k: self.key_mapping.get(k.name), movement_keys +
fightinput_keys))))
print(Style.RESET_ALL)
self.input_controller.handle_keys(movement_keys + fightinput_keys)
if self.dqn_movement.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_movement.erode_epsilon(factor=2)
self.dqn_fightinput.erode_epsilon(factor=2)
self.dqn_movement.next_step()
self.dqn_fightinput.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 6), maxlen=8),
"enemy_health": collections.deque(np.full((8, ), 654), maxlen=8),
"current_run": 1,
"current_run_steps": 0,
"run_reward_movement": 0,
"run_reward_fightinput": 0,
"run_future_rewards": 0,
"run_predicted_actions": 0,
"run_timestamp": datetime.utcnow(),
"last_run_duration": 0,
"record_time_alive": dict(),
"record_enemy_hp": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"random_enemy_hp": None,
"random_enemy_hps": list(),
"fightstarted": None,
"multiplier_damage": 0
}
def _calculate_reward(self):
reward_movement = 0
reward_fightinput = 0
# getting hit by enemy
if self.game_state["health"][0] < self.game_state["health"][1]:
self.game_state["multiplier_damage"] = 0
reward_movement += -0.10
reward_fightinput += -0.10
else:
reward_movement += 0.05
# hitting the enemy
if self.game_state["enemy_health"][0] < self.game_state[
"enemy_health"][1]:
# combo multiplicator
self.game_state["multiplier_damage"] += 0.20
if self.game_state["multiplier_damage"] > 1:
self.game_state["multiplier_damage"] = 1
# check how much dmg the attack did and add 0.05 per class to reward
if (self.game_state["enemy_health"][1] -
self.game_state["enemy_health"][0]) > 150: # light
reward_fightinput += 0.05
if (self.game_state["enemy_health"][1] -
self.game_state["enemy_health"][0]) > 500: # medium
reward_fightinput += 0.05
if (self.game_state["enemy_health"][1] -
self.game_state["enemy_health"][0]) > 750: # hard
reward_fightinput += 0.05
# calculate reward
reward_fightinput += (1 * self.game_state["multiplier_damage"])
else:
reward_fightinput += -0.05
reward_movement += -0.01
# enemy wasnt hit for 5 rounds
if self.game_state["enemy_health"][0] == self.game_state[
"enemy_health"][5]:
self.game_state["multiplier_damage"] = 0
# return rewards
return reward_movement, reward_fightinput
def handle_fight_end(self, game_frame):
self.game_state["fightstarted"] = None
self.input_controller.handle_keys([])
self.game_state["current_run"] += 1
self.handle_fight_training(game_frame)
def handle_fight_training(self, game_frame):
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
gc.enable()
gc.collect()
gc.disable()
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_movement.mode == "RUN",
"enemy_hp": self.game_state["enemy_health"][0]
}
if self.game_state["enemy_health"][0] < self.game_state[
"record_enemy_hp"].get("value", 1000):
self.game_state["record_enemy_hp"] = {
"value": self.game_state["enemy_health"][0],
"run": self.game_state["current_run"],
"predicted": self.dqn_movement.mode == "RUN",
"time_alive": self.game_state["last_run_duration"]
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_enemy_hps"].append(
self.game_state["enemy_health"][0])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["random_enemy_hp"] = np.mean(
self.game_state["random_enemy_hps"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
if self.dqn_movement.mode == "TRAIN":
for i in range(16):
serpent.utilities.clear_terminal()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print(f"TRAINING ON MINI-BATCHES: {i + 1}/16")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 25 == 0 else ''}"
)
print(Style.RESET_ALL)
self.dqn_movement.train_on_mini_batch()
self.dqn_fightinput.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["run_reward_movement"] = 0
self.game_state["run_reward_fightinput"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ), 6),
maxlen=8)
self.game_state["enemy_health"] = collections.deque(np.full((8, ),
654),
maxlen=8)
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
self.dqn_movement.update_target_model()
self.dqn_fightinput.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_movement.enter_run_mode()
self.dqn_fightinput.enter_run_mode()
else:
self.dqn_movement.enter_train_mode()
self.dqn_fightinput.enter_train_mode()
self.handle_retry_button(game_frame)
class SerpentGeometryDashGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.game_state = None
self._reset_game_state()
def setup_play(self):
input_mapping = {"SPACE": [KeyboardKey.KEY_SPACE]}
self.key_mapping = {KeyboardKey.KEY_SPACE.name: "SPACE"}
action_space = KeyboardMouseActionSpace(action_keys=[None, "SPACE"])
action_model_file_path = "datasets/GeometryDash_action_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_action = DDQN(
model_file_path=action_model_file_path
if os.path.isfile(action_model_file_path) else None,
input_shape=(self.game.frame_height, self.game.frame_width, 4),
input_mapping=input_mapping,
action_space=action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=10000,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=0.25,
final_epsilon=0.01,
override_epsilon=False)
def handle_play(self, game_frame):
gc.disable()
for i, game_frame in enumerate(self.game_frame_buffer.frames):
self.visual_debugger.store_image_data(
game_frame.grayscale_frame, game_frame.grayscale_frame.shape,
str(i))
if self.dqn_action.first_run:
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
self.dqn_action.first_run = False
time.sleep(5)
return None
actor_hp = self._measure_actor_hp(game_frame)
run_score = self._measure_run_score(game_frame)
self.game_state["health"].appendleft(actor_hp)
self.game_state["score"].appendleft(run_score)
if self.dqn_action.frame_stack is None:
full_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE").frames[0]
self.dqn_action.build_frame_stack(full_game_frame.frame)
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE")
if self.dqn_action.mode == "TRAIN":
reward_action = self._calculate_reward()
self.game_state["run_reward_action"] = max(
self.game_state["run_reward_action"], reward_action)
self.dqn_action.append_to_replay_memory(
game_frame_buffer,
reward_action,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_action.current_step % 2000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"datasets/GeometryDash_action")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_action.current_step % 20000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"datasets/GeometryDash_action",
is_checkpoint=True)
elif self.dqn_action.mode == "RUN":
self.dqn_action.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(
"GAME: GeometryDash PLATFORM: Steam AGENT: DDQN + Prioritized Experience Replay"
)
print("")
self.dqn_action.output_step_data()
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_action'], 2)}"
)
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(f"CURRENT SCORE: {self.game_state['score'][0]}")
print("")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
if self.game_state["health"][1] <= 0:
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_action.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_action.mode == "RUN"
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
if self.dqn_action.mode == "TRAIN":
for i in range(8):
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(
"GAME: GeometryDash PLATFORM: Steam AGENT: DDQN + Prioritized Experience Replay"
)
print("")
print(f"TRAINING ON MINI-BATCHES: {i + 1}/8")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}"
)
print(f"LAST RUN: {self.game_state['current_run']}")
print(
f"LAST RUN REWARD: {round(self.game_state['run_reward_action'], 2)}"
)
print(
f"LAST RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"LAST SCORE: {self.game_state['score'][0]}")
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
self.dqn_action.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_action"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ),
3),
maxlen=8)
self.game_state["score"] = collections.deque(np.full((8, ), 0),
maxlen=8)
if self.dqn_action.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
if self.dqn_action.type == "DDQN":
self.dqn_action.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_action.enter_run_mode()
else:
self.dqn_action.enter_train_mode()
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
time.sleep(1)
#self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
return None
self.dqn_action.pick_action()
self.dqn_action.generate_action()
keys = self.dqn_action.get_input_values()
print("")
print("PRESSING: ", end='')
print(" + ".join(
list(map(lambda k: self.key_mapping.get(k.name), keys))))
self.input_controller.handle_keys(keys)
if self.dqn_action.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_action.erode_epsilon(factor=2)
self.dqn_action.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 3), maxlen=8),
"score": collections.deque(np.full((8, ), 0), maxlen=8),
"run_reward_action": 0,
"current_run": 1,
"current_run_steps": 0,
"current_run_health": 0,
"current_run_score": 0,
"run_predicted_actions": 0,
"last_run_duration": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"run_timestamp": datetime.utcnow(),
}
def _measure_actor_hp(self, game_frame):
hp_area_grayscale = serpent.cv.extract_region_from_image(
game_frame.grayscale_frame, self.game.screen_regions["SCORE_AREA"])
try:
threshold = skimage.filters.threshold_otsu(hp_area_grayscale)
except ValueError:
threshold = 0
return (1 if threshold > 90 else 0)
def _measure_run_score(self, game_frame):
score_grayscale = serpent.cv.extract_region_from_image(
game_frame.grayscale_frame, self.game.screen_regions["SCORE_AREA"])
try:
threshold = skimage.filters.threshold_otsu(score_grayscale)
except ValueError:
threshold = 0
bw_score_bar = score_grayscale > threshold
score = str(bw_score_bar[bw_score_bar > 0].size)
self.game_state["current_run_score"] = score
return score
def _calculate_reward(self):
reward = int(self.game_state["score"][0])
return reward
class SerpentSlayTheSpireGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
global prevContext
prevContext = "None"
self.game_state = None
self._reset_game_state()
serpent.utilities.clear_terminal()
print("------------------------------------")
print("Starting up . . . ")
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
def setup_play(self):
global input_mapping
print("------------------------------------")
print("Loading Image Classifer . . . ")
print("------------------------------------")
plugin_path = offshoot.config["file_paths"]["plugins"]
context_classifier_path = f"{plugin_path}/SerpentSlayTheSpireGameAgentPlugin/files/ml_models/context_classifier.model"
context_classifier = CNNInceptionV3ContextClassifier(input_shape=(384, 512, 3)) # Replace with the shape (rows, cols, channels) of your captured context frames
context_classifier.prepare_generators()
context_classifier.load_classifier(context_classifier_path)
self.machine_learning_models["context_classifier"] = context_classifier
input_mapping = {
1: [KeyboardKey.KEY_1],
2: [KeyboardKey.KEY_2],
3: [KeyboardKey.KEY_3],
4: [KeyboardKey.KEY_4],
5: [KeyboardKey.KEY_5],
"E": [KeyboardKey.KEY_E]
}
action_space = KeyboardMouseActionSpace(
card_inputs=[1, 2, 3, 4, 5, "E"]
)
card_selection_model_file_path = "datasets/tdar31_slaythespire_dqn_0.9981189999999986_.h5".replace("/", os.sep)
# DDQN setup
self.dqn_card_selection = DDQN(
model_file_path=card_selection_model_file_path if os.path.isfile(card_selection_model_file_path) else None,
input_shape=(90, 160, 4),
input_mapping=input_mapping,
action_space=action_space,
replay_memory_size=1000,
max_steps=1000,
observe_steps=100,
batch_size=64,
model_learning_rate=1e-4,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=True
)
def find_index(self):
return print("TEST")
def handle_play(self, game_frame):
global prevContext
context = self.machine_learning_models["context_classifier"].predict(game_frame.frame)
print(context)
if context != prevContext:
prevContext = context
time.sleep(1)
return print("context doesn't match prevContext")
if context == "DEATH_MENU":
self.handle_DEATH_MENU(game_frame, context)
elif context == "BATTLE_STAGE":
self.handle_BATTLE_STAGE(game_frame, context)
elif context == "REWARD_STAGE":
self.handle_REWARD_STAGE(game_frame, context)
# While these classes aren't used; the image classifer is trained to check for them
# and maybe prove useful/important as I expand on the project. So even though they
# currently do nothing I am going to leave them for now
elif context == "START_RUN":
self.handle_START_RUN(game_frame, context)
elif context == "MAP_MENU":
self.handle_MAP_MENU(game_frame, context)
elif context == "MERCHANT_MENU":
self.handle_MERCHANT_MENU(game_frame, context)
elif context == "MERCHANT_PRE_MENU":
self.handle_MERCHANT_PRE_MENU(game_frame, context)
elif context == "REST_STAGE":
self.handle_REST_STAGE(game_frame, context)
elif context == "SMITH_DECK_LIST":
self.handle_SMITH_DECK_LIST(game_frame, context)
def _reset_game_state(self):
self.game_state = {
"current_run": 0,
"current_run_steps": 0,
"last_run_duration": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"run_timestamp": datetime.utcnow(),
"masterCardList": ["Strike_G", "Strike_G", "Strike_G", "Strike_G", "Defend_G", "Defend_G", "Defend_G", "Defend_G", "Poisoned Stab", "Neutralize", "Dodge and Roll"],
"player_energy_available": [3],
"player_energy_total": [3],
"player_health": [70],
"final_cultist_attack": [0],
"poison_check": [False],
"run_reward_selection": 0,
"run_predicted_selection": 0,
}
def handle_DEATH_MENU(self, game_frame, context):
print("INSIDE DEATH_MENU function")
global prevContext
prevContext = "DEATH_STAGE"
time.sleep(1)
death_menuing_Xcoords = [639, 644, 104, 344, 634, 1207]
death_menuing_Ycoords = [622, 637, 440, 376, 579, 593]
menuing_delays = [1, 2, 1, 1, 1, 1]
for elem in range(6):
self.input_controller.move(x=death_menuing_Xcoords[elem], y=death_menuing_Ycoords[elem], duration=0.25, absolute=True)
self.input_controller.click(button=MouseButton.LEFT, duration=0.25)
time.sleep(menuing_delays[elem])
time.sleep(1)
self.fight_setup()
# Remove relic and reset deck
def fight_setup(self):
console_commands = ["relic remove r", "deck remove a", "fight cu"]
self.input_controller.type_string("~", 0.05)
for elem in range(3):
self.input_controller.type_string(console_commands[elem], 0.05)
self.input_controller.tap_key(KeyboardKey.KEY_TAB)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(1)
self.input_controller.type_string("~", 0.05)
time.sleep(1)
self.game_state["current_run"] += 1
print("self.dqn_card_selection.mode: --- ", self.dqn_card_selection.mode)
if self.dqn_card_selection.mode in ["TRAIN", "RUN"]:
print("if self.dqn_card_selection.mode in ['TRAIN', 'RUN']:")
print("----------------------------------------------------")
time.sleep(2)
if self.game_state["current_run"] > 0 and self.game_state["current_run"] % 100 == 0:
if self.dqn_card_selection.type == "DDQN":
self.dqn_card_selection.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state["current_run"] % 20 == 0:
self.dqn_card_selection.enter_run_mode()
else:
self.dqn_card_selection.enter_train_mode()
# NOW INSIDE BATTLE STAGE BUT FUNCTION HASN'T BEEN TRIGGERED BY IMAGE CONTEXT CLASSIFER
self.populating_deck()
def populating_deck(self):
masterCardList = self.game_state["masterCardList"]
time.sleep(1)
self.input_controller.type_string("~", 0.05)
time.sleep(1)
prefixCmd = "hand add "
for elem in range(len(masterCardList)):
print(masterCardList[elem])
tempCard = ""
tempCardSelection = ""
tempCard = masterCardList[elem]
tempCardSelection = prefixCmd + tempCard
self.input_controller.type_string(tempCardSelection, 0.05)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(1)
# Closes console
self.input_controller.type_string("~", 0.05)
# Ends turn
self.input_controller.tap_key(KeyboardKey.KEY_E)
time.sleep(1.5)
def handle_BATTLE_STAGE(self, game_frame, context):
print("INSIDE BATTLE_STAGE function")
global prevContext
prevContext = "BATTLE_STAGE"
# Player Energy
player_energy = serpent.cv.extract_region_from_image(game_frame.frame, self.game.screen_regions["PLAYER_ENERGY"])
player_energy_grayscale = np.array(skimage.color.rgb2gray(player_energy) * 255, dtype="uint8")
player_energy = serpent.ocr.perform_ocr(image=player_energy_grayscale, scale=15, order=5, horizontal_closing=2, vertical_closing=1)
print("player_energy")
print(player_energy)
# Parses returned value from tesseract for grabbing current energy
# Issue is that because of the swirling animation behind the numbers the OCR isn't 100% at returning this value correctly
# The '/' is by far the most consistant value returned and it's the char that the energy values revolve around so if it's not found we force the program to refresh and grab a new game_image and try again
if '/' in player_energy:
print("player_energy INSIDE IF STATEMENT")
finalArr = []
## Examples of type of values returned by OCR when grabbing energy
# 3/3 <- correct
# '3/3
# "3 /3
# 53 /3
# 27/ 3
# "3/3'
# 3/3.
# .3 /3
# *3/3
for elem in player_energy:
# Next layer of parsing
# If the value isn't '/' or an integer it isn't pushed into finalArr
if (elem == "/") or (elem.isdigit() == True):
finalArr.append(elem)
# Final check
# if the length of the list is greater 3 or '/' isn't in the second position
# Techincally this fails if the player has 10 or more energy but due to how infrequently this happens I don't have a conditional check for it
if len(finalArr) < 3 or (finalArr[1] != "/"):
return print("Failed to capture energy successfully // len(finalArr) < 3) or finalArr[1] != '/'")
# Capture available and total player energy
player_energy_available = finalArr[0]
player_energy_total = finalArr[2]
print("------------------------------------")
print(player_energy_available, "/", player_energy_total)
print("------------------------------------")
time.sleep(1)
self.game_state["player_energy_available"].insert(0, player_energy_available)
self.game_state["player_energy_total"].insert(0, player_energy_total)
else:
print(player_energy)
return print("Failed to capture energy successfully // captured energy value doesn't have '/'")
# Player Health
player_total_health = serpent.cv.extract_region_from_image(game_frame.frame, self.game.screen_regions["PLAYER_HEALTH"])
player_total_health_grayscale = np.array(skimage.color.rgb2gray(player_total_health) * 255, dtype="uint8")
player_health = serpent.ocr.perform_ocr(image=player_total_health_grayscale, scale=15, order=5, horizontal_closing=2, vertical_closing=1)
tempArr = []
for elem in player_health:
if (elem.isdigit() == True):
tempArr.append(elem)
if (elem == "/"):
break
player_health = ''.join(tempArr)
print("player_health", player_health)
self.game_state["player_health"].insert(0, player_health)
time.sleep(.5)
self.enemy_action_capture(game_frame)
def enemy_action_capture(self, game_frame):
final_cultist_attack = []
attack_cultist_temp_list= []
# Unselect anything just incase
self.input_controller.click(button=MouseButton.RIGHT, duration=0.25)
time.sleep(.5)
# Home hover
self.input_controller.move(x=636, y=375, duration=0.25, absolute=True)
time.sleep(1)
# Enemy hover
self.input_controller.move(x=959, y=410, duration=0.25, absolute=True)
time.sleep(.75)
image_data = skimage.io.imread("plugins/SerpentSlayTheSpireGamePlugin/files/data/sprites/sprite_Attack_for_0.png")[..., np.newaxis]
attack_for_cultist = Sprite("attack_for_cultist", image_data=image_data)
# Full game frame capture
# print("-----------Full game frame capture-----------")
# full_game_frame = FrameGrabber.get_frames(
# [0],
# frame_shape=(self.game.frame_height, self.game.frame_width),
# frame_type="PIPELINE"
# ).frames[0]
# Allows for dynamic capture of enemy attack
sprite_locator = SpriteLocator()
attack_for_cultist_location = sprite_locator.locate(sprite=attack_for_cultist, game_frame=game_frame)
print("attack_for_cultist_location: ", attack_for_cultist_location)
# Tuples are immutable :(
if (attack_for_cultist_location != None):
attack_cultist_temp_list = list(attack_for_cultist_location)
attack_cultist_temp_list[1] = attack_cultist_temp_list[1] + 45
attack_cultist_temp_list[3] = attack_cultist_temp_list[3] + 15
attack_for_cultist_location = tuple(attack_cultist_temp_list)
print("Updated - attack_for_cultist_location: ", attack_for_cultist_location)
time.sleep(1)
cultist_attack = serpent.cv.extract_region_from_image(game_frame.frame, attack_for_cultist_location)
cultist_attack_grayscale = np.array(skimage.color.rgb2gray(cultist_attack) * 255, dtype="uint8")
cultist_attack = serpent.ocr.perform_ocr(image=cultist_attack_grayscale, scale=15, order=5, horizontal_closing=2, vertical_closing=1)
# This is actually an awkward work around for limitations in how tesseract works. By default it doesn't capture single char values so when dynamically
# searching and capturing the enemy attack the region it's looking for the region that includes the word "for " + attack value (i.e. "for 6"). There
# are ways of swapping the mode of tesseract to do a capture for single char values but because the attack values are dynamic it sometimes is
# less than 10 or much greater than 10 which is now multiple char's and messes with the capture. For the sake of just getting it working I did this
# TLDR: Awkward workaround for limitation in tesseract when capturing single char values. Likely easier way to capture then parse attack value
for elem in cultist_attack:
if (elem.isdigit() == True):
final_cultist_attack.append(elem)
print("final_cultist_attack", final_cultist_attack)
final_cultist_attack = ''.join(final_cultist_attack)
print("final_cultist_attack: ", final_cultist_attack)
print("------------------------------------")
self.game_state["final_cultist_attack"].insert(0, final_cultist_attack)
self.poison_check(game_frame)
else:
return print("Failed to capture enemy attack")
def poison_check(self, game_frame):
image_data = skimage.io.imread("plugins/SerpentSlayTheSpireGamePlugin/files/data/sprites/sprite_poison_check_0.png")[..., np.newaxis]
poison_check = Sprite("poison_check", image_data=image_data)
# # Full game frame capture
# print("-----------Full game frame capture-----------")
# full_game_frame = FrameGrabber.get_frames(
# [0],
# frame_shape=(self.game.frame_height, self.game.frame_width),
# frame_type="PIPELINE"
# ).frames[0]
sprite_locator = SpriteLocator()
poison_check_location = sprite_locator.locate(sprite=poison_check, game_frame=game_frame)
print("poison_check_location: ", poison_check_location)
if (poison_check_location != None):
self.game_state["poison_check"].insert(0, True)
print("POISON_CHECK == TRUE")
else:
self.game_state["poison_check"].insert(0, False)
print("POISON_CHECK == FALSE")
self.ddqn_setup(game_frame)
def ddqn_setup(self, game_frame):
gc.disable()
if self.dqn_card_selection.first_run:
self.dqn_card_selection.first_run = False
print("---------------first_run---------------")
return None
timestamp_now = datetime.utcnow()
runtime_total = timestamp_now - self.game_state["run_timestamp"]
time.sleep(1)
print("self.dqn_card_selection.mode", self.dqn_card_selection.mode)
if self.dqn_card_selection.frame_stack is None:
full_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE"
).frames[0]
print("self.dqn_card_selection.frame_stack is None")
self.dqn_card_selection.build_frame_stack(full_game_frame.frame)
self.dqn_card_selection.frame_stack = self.dqn_card_selection.frame_stack
else:
print("ELSE -- self.dqn_card_selection.frame_stack is None // game_frame_buffer")
print("INSIDE ELSE self.dqn_card_selection.mode", self.dqn_card_selection.mode)
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE"
)
if self.dqn_card_selection.mode == "TRAIN":
print("self.dqn_card_selection.mode == TRAIN", self.dqn_card_selection.mode)
time.sleep(2)
# calculates reward then appends it to replay memory
reward_selection = self.calculate_reward()
self.game_state["run_reward_selection"] += reward_selection
self.dqn_card_selection.append_to_replay_memory(
game_frame_buffer,
reward_selection,
terminal=self.game_state["player_health"] == 0
)
if self.dqn_card_selection.current_step % 100 == 0:
self.dqn_card_selection.save_model_weights(
file_path_prefix=f"datasets/tdar31_slaythespire_selection"
)
if self.dqn_card_selection.current_step % 500 == 0:
self.dqn_card_selection.save_model_weights(
file_path_prefix=f"datasets/tdar31_slaythespire_selection"
)
if self.dqn_card_selection.current_step % 5000 == 0:
self.dqn_card_selection.save_model_weights(
file_path_prefix=f"datasets/tdar31_slaythespire_selection",
is_checkpoint=True
)
elif self.dqn_card_selection.mode == "RUN":
self.dqn_card_selection.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds")
print("")
print("Selection NN:\n")
self.dqn_card_selection.output_step_data()
print("")
print(f"RUN: {self.game_state['current_run']}")
print(f"RUN REWARD: {round(self.game_state['run_reward_selection'], 2)}")
print(f"RUN PREDICTED ACTIONS: {self.game_state['run_predicted_selection']}")
print(f"PLAYER HEALTH: {self.game_state['player_health'][0]}")
print(f"PLAYER ENERGY AVAILABLE: {self.game_state['player_energy_available'][0]}")
print(f"PLAYER ENERGY TOTAL: {self.game_state['player_energy_total'][0]}")
print(f"PLAYER ENERGY AVAILABLE: {self.game_state['player_energy_available'][0]}")
print(f"POISON CHECK: {self.game_state['poison_check'][0]}")
print(f"RUN TIME: {runtime_total.seconds} seconds")
print(f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds")
self.dqn_card_selection.pick_action()
self.dqn_card_selection.generate_action()
card_selection_keys = self.dqn_card_selection.get_input_values()
print("card_selection_keys", card_selection_keys)
ddqnInputSelection = card_selection_keys[0]
print("ddqnInputSelection", ddqnInputSelection)
print("self.dqn_card_selection.current_action_type", self.dqn_card_selection.current_action_type)
# Starts as random? Once frame stack is built out swaps to PREDICTED? as in delibrate choice when running .action
# appending to memory + setting up reward calucation?
if self.dqn_card_selection.current_action_type == "PREDICTED":
self.game_state["run_predicted_selection"] += 1
self.dqn_card_selection.erode_epsilon(factor=2)
self.dqn_card_selection.next_step()
self.ddqn_action_output(ddqnInputSelection)
def ddqn_action_output(self, ddqnInputSelection):
print(ddqnInputSelection)
# Unselects anything just incase
self.input_controller.click(button=MouseButton.RIGHT, duration=0.25)
if ddqnInputSelection == "KeyboardKey.KEY_E":
print("ddqnInputSelection = E // End turn")
self.input_controller.tap_key(ddqnInputSelection)
time.sleep(.5)
else:
# This is where the chosen card is actually selected then played
self.input_controller.tap_key(ddqnInputSelection)
time.sleep(.5)
play_card_Xcoords = [636, 959]
play_card_Ycoords = [375, 410]
for elem in range(2):
self.input_controller.move(x=play_card_Xcoords[elem], y=play_card_Ycoords[elem], duration=0.25, absolute=True)
time.sleep(1)
self.input_controller.click(button=MouseButton.LEFT, duration=0.25)
time.sleep(.5)
def calculate_reward(self):
reward = 0
# 1 to 9 damage taken that turn // -5
reward -= (5 if (int(self.game_state["player_health"][1])) - (int(self.game_state["player_health"][0])) <= 9 else 0)
# 10 or more damage taken that turn // -10
reward -= (10 if (int(self.game_state["player_health"][1])) - (int(self.game_state["player_health"][0])) >= 10 else 0)
# Slight penalty if no card is played aka no energy used // -3
reward -= (3 if (int(self.game_state["player_energy_available"][0])) == (int(self.game_state["player_energy_total"][0])) else 0)
# Energy used to play card regardless of what it does // +10
reward += (10 if (int(self.game_state["player_energy_available"][0])) < (int(self.game_state["player_energy_total"][1])) else 0)
# If enemy poisoned // +5
reward += (5 if (self.game_state["poison_check"][0] == True) else 0)
return reward
def handle_REWARD_STAGE(self, game_frame, context):
print("INSIDE REWARD_STAGE function")
global prevContext
prevContext = "REWARD_STAGE"
time.sleep(.5)
play_card_Xcoords = [1249, 952, 563]
play_card_Ycoords = [26, 146, 456]
for elem in range(3):
self.input_controller.move(x=play_card_Xcoords[elem], y=play_card_Ycoords[elem], duration=0.25, absolute=True)
time.sleep(.75)
self.input_controller.click(button=MouseButton.LEFT, duration=0.25)
time.sleep(.5)
# def handle_MERCHANT_PRE_MENU(self, game_frame, context):
# print("INSIDE MERCHANT_PRE_MENU function")
# global prevContext
# prevContext = "MERCHANT_PRE_MENU"
# time.sleep(1)
# def handle_MERCHANT_MENU(self, game_frame, context):
# print("INSIDE MERCHANT_MENU function")
# global prevContext
# prevContext = "MERCHANT_MENU"
# def handle_MAP_MENU(self, game_frame, context):
# print("INSIDE MAP_MENU function")
# global prevContext
# prevContext = "MAP_MENU"
# time.sleep(1)
# def handle_REST_STAGE(self, game_frame, context):
# print("INSIDE REST_STAGE function")
# global prevContext
# prevContext = "REST_STAGE"
# time.sleep(1)
# def handle_SMITH_DECK_LIST(self, game_frame, context):
# print("INSIDE SMITH_DECK_LIST function")
# global prevContext
# prevContext = "SMITH_DECK_LIST"
# time.sleep(1)
class SerpentSpaceInvadersGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.analytics_client = None
self.game_state = None
self._reset_game_state()
init()
def setup_play(self):
# ALL Key/Input Mappings for SNES Emulator
input_mapping = {
"UP": [KeyboardKey.KEY_W],
"LEFT": [KeyboardKey.KEY_A],
"DOWN": [KeyboardKey.KEY_S],
"RIGHT": [KeyboardKey.KEY_D],
"START": [KeyboardKey.KEY_ENTER],
"SELECT": [KeyboardKey.KEY_BACKSPACE],
"B": [KeyboardKey.KEY_Z],
"A": [KeyboardKey.KEY_V],
"Y": [KeyboardKey.KEY_X],
"X": [KeyboardKey.KEY_C],
"L": [KeyboardKey.KEY_B],
"R": [KeyboardKey.KEY_N]
}
self.key_mapping = {
KeyboardKey.KEY_W.name: "UP",
KeyboardKey.KEY_A.name: "LEFT",
KeyboardKey.KEY_S.name: "DOWN",
KeyboardKey.KEY_D.name: "RIGHT",
KeyboardKey.KEY_ENTER.name: "START",
KeyboardKey.KEY_BACKSPACE.name: "SELECT",
KeyboardKey.KEY_Z.name: "B",
KeyboardKey.KEY_V.name: "A",
KeyboardKey.KEY_X.name: "Y",
KeyboardKey.KEY_C.name: "X",
KeyboardKey.KEY_B.name: "L",
KeyboardKey.KEY_N.name: "R"
}
# Game Specific Inputs
direction_action_space = KeyboardMouseActionSpace(
direction_keys=[None, "LEFT", "RIGHT"])
action_space = KeyboardMouseActionSpace(
action_keys=[None, "B", "A", "Y", "X"])
direction_model_file_path = "datasets/spaceinvaders_direction_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_direction = DDQN(
model_file_path=direction_model_file_path
if os.path.isfile(direction_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=direction_action_space,
replay_memory_size=40000,
max_steps=3000000,
observe_steps=5000,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=1.0,
final_epsilon=0.1,
override_epsilon=False)
action_model_file_path = "datasets/spaceinvaders_action_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_action = DDQN(
model_file_path=action_model_file_path
if os.path.isfile(action_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=action_space,
replay_memory_size=40000,
max_steps=3000000,
observe_steps=5000,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=1.0,
final_epsilon=0.1,
override_epsilon=False)
def handle_play(self, game_frame):
gc.disable()
if self.dqn_direction.first_run:
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_S)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_N) # 1 Credit
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(5)
self.dqn_direction.first_run = False
self.dqn_action.first_run = False
return None
vessel_hp = self._measure_hp(game_frame)
vessel_score = self._measure_score(game_frame)
# vessel_credits = self._measure_credits(game_frame)
self.game_state["health"].appendleft(vessel_hp)
self.game_state["score"].appendleft(vessel_score)
# self.game_state["credits"].appendleft(vessel_credits)
if self.dqn_direction.frame_stack is None:
pipeline_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE").frames[0]
self.dqn_direction.build_frame_stack(pipeline_game_frame.frame)
self.dqn_action.frame_stack = self.dqn_direction.frame_stack
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE")
if self.dqn_direction.mode == "TRAIN":
reward_direction, reward_action = self._calculate_reward()
self.game_state["run_reward_direction"] += reward_direction
self.game_state["run_reward_action"] += reward_action
self.dqn_direction.append_to_replay_memory(
game_frame_buffer,
reward_direction,
terminal=self.game_state["health"] == 0)
self.dqn_action.append_to_replay_memory(
game_frame_buffer,
reward_action,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_direction.current_step % 2000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/spaceinvaders_direction")
self.dqn_action.save_model_weights(
file_path_prefix=f"datasets/spaceinvaders_action")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_direction.current_step % 20000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/spaceinvaders_direction",
is_checkpoint=True)
self.dqn_action.save_model_weights(
file_path_prefix=f"datasets/spaceinvaders_action",
is_checkpoint=True)
elif self.dqn_direction.mode == "RUN":
self.dqn_direction.update_frame_stack(game_frame_buffer)
self.dqn_action.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
"\033[31m" +
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
+ "\033[37m")
print("GAME: Space Invaders PLATFORM: SNES AGENT: DDQN + PER")
print("")
print("\033[32m" + "DIRECTION NEURAL NETWORK INFO:\n" + "\033[37m")
self.dqn_direction.output_step_data()
print("")
print("\033[32m" + "ACTION NEURAL NETWORK INFO:\n" + "\033[37m")
self.dqn_action.output_step_data()
print("")
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_direction'] + self.game_state['run_reward_action'], 2)}"
)
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(f"CURRENT SCORE: {self.game_state['score'][0]}")
# print(f"CURRENT CREDITS: {self.game_state['credits'][0]}")
print("")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
if self.game_state["health"][2] <= 0:
serpent.utilities.clear_terminal()
print("ENTERING THE HEALTH <= 0 PART")
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_direction.mode == "RUN"
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
if self.dqn_direction.mode == "TRAIN":
for i in range(16):
serpent.utilities.clear_terminal()
print(
"\033[31m" +
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
+ "\033[37m")
print(
"\033[32m" +
"GAME: Space Invaders PLATFORM: Steam AGENT: DDQN + PER"
+ "\033[37m")
print("")
print("TRAINING ON MINI-BATCHES:" + "\033[32m" +
f"{i + 1}/16" + "\033[37m")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}"
)
self.dqn_direction.train_on_mini_batch()
self.dqn_action.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_direction"] = 0
self.game_state["run_reward_action"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ),
3),
maxlen=8)
self.game_state["score"] = collections.deque(np.full((8, ), 0),
maxlen=8)
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
if self.dqn_direction.type == "DDQN":
self.dqn_direction.update_target_model()
self.dqn_action.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_direction.enter_run_mode()
self.dqn_action.enter_run_mode()
else:
self.dqn_direction.enter_train_mode()
self.dqn_action.enter_train_mode()
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_N)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(6)
return None
self.dqn_direction.pick_action()
self.dqn_direction.generate_action()
self.dqn_action.pick_action(
action_type=self.dqn_direction.current_action_type)
self.dqn_action.generate_action()
keys = self.dqn_direction.get_input_values(
) + self.dqn_action.get_input_values()
print("")
print("PRESSING: ", end='')
print(" + ".join(
list(map(lambda k: self.key_mapping.get(k.name), keys))))
self.input_controller.handle_keys(keys)
if self.dqn_direction.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_direction.erode_epsilon(factor=2)
self.dqn_action.erode_epsilon(factor=2)
self.dqn_direction.next_step()
self.dqn_action.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 3), maxlen=8),
"score": collections.deque(np.full((8, ), 0), maxlen=8),
"run_reward_direction": 0,
"run_reward_action": 0,
"current_run": 1,
"current_run_steps": 0,
"current_run_health": 3,
"current_run_score": 0,
"run_predicted_actions": 0,
"last_run_duration": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"run_timestamp": datetime.utcnow(),
}
def _measure_score(self, game_frame):
score_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["GAME_CURRENT_SCORE"])
score_grayscale = np.array(skimage.color.rgb2gray(score_area_frame) *
255,
dtype="uint8")
score = serpent.ocr.perform_ocr(image=score_grayscale,
scale=10,
order=1,
horizontal_closing=1,
vertical_closing=1)
count = 0
if len(score) == 4 and score.isdigit() and score != '0000':
for char in score:
if char == '0':
count = count + 1
else:
break
score = score[count:]
else:
score = '0'
self.game_state["current_run_score"] = score
return score
def _measure_hp(self, game_frame):
hp_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["GAME_CURRENT_HEALTH"])
vessel_hp = 0
max_ssim = 0
for name, sprite in self.game.sprites.items():
for i in range(sprite.image_data.shape[3]):
ssim = skimage.measure.compare_ssim(
hp_area_frame,
np.squeeze(sprite.image_data[..., i]),
multichannel=True)
if ssim > max_ssim:
max_ssim = ssim
vessel_hp = int(name[-1])
return vessel_hp
# def _measure_credits(self, game_frame):
# # OCR or Sprites if inconsistent (see TiamatX health)
# credits_area_frame = serpent.cv.extract_region_from_image(game_frame.frame, self.game.screen_regions["GAME_CURRENT_CREDITS"])
#
# credits_grayscale = np.array(skimage.color.rgb2gray(credits_area_frame) * 255, dtype="uint8")
#
# credits = serpent.ocr.perform_ocr(image=credits_grayscale, scale=10, order=5, horizontal_closing=10, vertical_closing=5)
#
# count = 0
#
# if len(credits) == 2 and credits.isdigit():
# for char in credits:
# if char == '0':
# count = count + 1
# else:
# break
# credits = credits[count:]
# else:
# credits = '50'
#
# self.game_state["current_run_credits"] = credits
#
# return credits
def _calculate_reward(self):
reward = 0
# reward += (-1.0 if self.game_state["credits"][0] < self.game_state["credits"][1] else 0.1)
reward += (-0.5 if
self.game_state["health"][0] < self.game_state["health"][1]
else 0.05)
reward += (0.75 if
(int(self.game_state["score"][0]) -
int(self.game_state["score"][1])) >= 10 else -0.075)
return reward, reward
class SerpentBindingOfIsaacAfterbirthGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.game_state = None
self._reset_game_state()
@property
def bosses(self):
return {"MONSTRO": "1010"}
def setup_play(self):
input_mapping = {
"W": [KeyboardKey.KEY_W],
"A": [KeyboardKey.KEY_A],
"S": [KeyboardKey.KEY_S],
"D": [KeyboardKey.KEY_D],
"WA": [KeyboardKey.KEY_W, KeyboardKey.KEY_A],
"WD": [KeyboardKey.KEY_W, KeyboardKey.KEY_D],
"SA": [KeyboardKey.KEY_S, KeyboardKey.KEY_A],
"SD": [KeyboardKey.KEY_S, KeyboardKey.KEY_D],
"UP": [KeyboardKey.KEY_UP],
"LEFT": [KeyboardKey.KEY_LEFT],
"DOWN": [KeyboardKey.KEY_DOWN],
"RIGHT": [KeyboardKey.KEY_RIGHT]
}
self.key_mapping = {
KeyboardKey.KEY_W.name: "MOVE UP",
KeyboardKey.KEY_A.name: "MOVE LEFT",
KeyboardKey.KEY_S.name: "MOVE DOWN",
KeyboardKey.KEY_D.name: "MOVE RIGHT",
KeyboardKey.KEY_UP.name: "SHOOT UP",
KeyboardKey.KEY_LEFT.name: "SHOOT LEFT",
KeyboardKey.KEY_DOWN.name: "SHOOT DOWN",
KeyboardKey.KEY_RIGHT.name: "SHOOT RIGHT",
}
movement_action_space = KeyboardMouseActionSpace(directional_keys=[
None, "W", "A", "S", "D", "WA", "WD", "SA", "SD"
])
projectile_action_space = KeyboardMouseActionSpace(
projectile_keys=[None, "UP", "LEFT", "DOWN", "RIGHT"])
movement_model_file_path = "datasets/binding_of_isaac_movement_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_movement = DDQN(
model_file_path=movement_model_file_path
if os.path.isfile(movement_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=movement_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=1000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False)
projectile_model_file_path = "datasets/binding_of_isaac_projectile_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_projectile = DDQN(
model_file_path=projectile_model_file_path
if os.path.isfile(projectile_model_file_path) else None,
input_shape=(100, 100, 4),
input_mapping=input_mapping,
action_space=projectile_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=1000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False)
if sys.platform in ["linux", "linux2"]:
pyperclip.set_clipboard("xsel")
pyperclip.copy(f"goto s.boss.{self.bosses['MONSTRO']}")
def handle_play(self, game_frame):
gc.disable()
if self.dqn_movement.first_run:
self._goto_boss()
self.dqn_movement.first_run = False
self.dqn_projectile.first_run = False
return None
hearts = frame_to_hearts(game_frame.frame, self.game)
# Check for Curse of Unknown
if not len(hearts):
self.input_controller.tap_key(KeyboardKey.KEY_R, duration=1.5)
self._goto_boss()
return None
self.game_state["health"].appendleft(24 - hearts.count(None))
self.game_state["boss_health"].appendleft(
self._get_boss_health(game_frame))
if self.dqn_movement.frame_stack is None:
pipeline_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE",
dtype="float64").frames[0]
self.dqn_movement.build_frame_stack(pipeline_game_frame.frame)
self.dqn_projectile.frame_stack = self.dqn_movement.frame_stack
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE",
dtype="float64")
if self.dqn_movement.mode == "TRAIN":
reward_movement, reward_projectile = self._calculate_reward()
self.game_state["run_reward_movement"] += reward_movement
self.game_state["run_reward_projectile"] += reward_projectile
self.dqn_movement.append_to_replay_memory(
game_frame_buffer,
reward_movement,
terminal=self.game_state["health"] == 0)
self.dqn_projectile.append_to_replay_memory(
game_frame_buffer,
reward_projectile,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_movement.current_step % 2000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/binding_of_isaac_movement")
self.dqn_projectile.save_model_weights(
file_path_prefix=f"datasets/binding_of_isaac_projectile"
)
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_movement.current_step % 20000 == 0:
self.dqn_movement.save_model_weights(
file_path_prefix=f"datasets/binding_of_isaac_movement",
is_checkpoint=True)
self.dqn_projectile.save_model_weights(
file_path_prefix=
f"datasets/binding_of_isaac_projectile",
is_checkpoint=True)
elif self.dqn_movement.mode == "RUN":
self.dqn_movement.update_frame_stack(game_frame_buffer)
self.dqn_projectile.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print("")
print("MOVEMENT NEURAL NETWORK:\n")
self.dqn_movement.output_step_data()
print("")
print("PROJECTILE NEURAL NETWORK:\n")
self.dqn_projectile.output_step_data()
print("")
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_movement'] + self.game_state['run_reward_projectile'], 2)}"
)
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(f"CURRENT BOSS HEALTH: {self.game_state['boss_health'][0]}")
print("")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'}, Boss HP {self.game_state['record_time_alive'].get('boss_hp')})"
)
print(
f"RECORD BOSS HP: {self.game_state['record_boss_hp'].get('value')} (Run {self.game_state['record_boss_hp'].get('run')}, {'Predicted' if self.game_state['record_boss_hp'].get('predicted') else 'Training'}, Time Alive {self.game_state['record_boss_hp'].get('time_alive')} seconds)"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
print(
f"RANDOM AVERAGE BOSS HP: {self.game_state['random_boss_hp']}")
is_boss_dead = self._is_boss_dead(
self.game_frame_buffer.previous_game_frame)
if self.game_state["health"][1] <= 0 or is_boss_dead:
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_movement.mode == "RUN",
"boss_hp": self.game_state["boss_health"][0]
}
if self.game_state["boss_health"][0] < self.game_state[
"record_boss_hp"].get("value", 1000):
self.game_state["record_boss_hp"] = {
"value": self.game_state["boss_health"][0],
"run": self.game_state["current_run"],
"predicted": self.dqn_movement.mode == "RUN",
"time_alive": self.game_state["last_run_duration"]
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_boss_hps"].append(
self.game_state["boss_health"][0])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["random_boss_hp"] = np.mean(
self.game_state["random_boss_hps"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
self.input_controller.tap_key(KeyboardKey.KEY_R, duration=1.5)
if self.dqn_movement.mode == "TRAIN":
for i in range(16):
serpent.utilities.clear_terminal()
print(f"TRAINING ON MINI-BATCHES: {i + 1}/16")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}"
)
self.dqn_movement.train_on_mini_batch()
self.dqn_projectile.train_on_mini_batch()
self.game_state["boss_skull_image"] = None
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_movement"] = 0
self.game_state["run_reward_projectile"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ),
6),
maxlen=8)
self.game_state["boss_health"] = collections.deque(np.full(
(8, ), 654),
maxlen=8)
if self.dqn_movement.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
self.dqn_movement.update_target_model()
self.dqn_projectile.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_movement.enter_run_mode()
self.dqn_projectile.enter_run_mode()
else:
self.dqn_movement.enter_train_mode()
self.dqn_projectile.enter_train_mode()
self._goto_boss()
return None
self.dqn_movement.pick_action()
self.dqn_movement.generate_action()
self.dqn_projectile.pick_action(
action_type=self.dqn_movement.current_action_type)
self.dqn_projectile.generate_action()
movement_keys = self.dqn_movement.get_input_values()
projectile_keys = self.dqn_projectile.get_input_values()
print("")
print(" + ".join(
list(
map(lambda k: self.key_mapping.get(k.name),
movement_keys + projectile_keys))))
self.input_controller.handle_keys(movement_keys + projectile_keys)
if self.dqn_movement.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_movement.erode_epsilon(factor=2)
self.dqn_projectile.erode_epsilon(factor=2)
self.dqn_movement.next_step()
self.dqn_projectile.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 6), maxlen=8),
"boss_health": collections.deque(np.full((8, ), 654), maxlen=8),
"boss_skull_image": None,
"current_run": 1,
"current_run_steps": 0,
"run_reward_movement": 0,
"run_reward_projectile": 0,
"run_future_rewards": 0,
"run_predicted_actions": 0,
"run_timestamp": datetime.utcnow(),
"last_run_duration": 0,
"record_time_alive": dict(),
"record_boss_hp": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"random_boss_hp": None,
"random_boss_hps": list()
}
def _goto_boss(self):
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
time.sleep(1)
self.input_controller.tap_key(KeyboardKey.KEY_GRAVE)
time.sleep(0.5)
self.input_controller.tap_keys(
[KeyboardKey.KEY_LEFT_CTRL, KeyboardKey.KEY_V])
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.5)
self.input_controller.tap_key(KeyboardKey.KEY_ENTER)
time.sleep(0.2)
def _get_boss_health(self, game_frame):
gray_boss_health_bar = serpent.cv.extract_region_from_image(
game_frame.grayscale_frame,
self.game.screen_regions["HUD_BOSS_HP"])
try:
threshold = skimage.filters.threshold_otsu(gray_boss_health_bar)
except ValueError:
threshold = 1
bw_boss_health_bar = gray_boss_health_bar > threshold
return bw_boss_health_bar[bw_boss_health_bar > 0].size
def _is_boss_dead(self, game_frame):
gray_boss_skull = serpent.cv.extract_region_from_image(
game_frame.grayscale_frame,
self.game.screen_regions["HUD_BOSS_SKULL"])
if self.game_state["boss_skull_image"] is None:
self.game_state["boss_skull_image"] = gray_boss_skull
is_dead = False
if skimage.measure.compare_ssim(
gray_boss_skull, self.game_state["boss_skull_image"]) < 0.5:
is_dead = True
self.game_state["boss_skull_image"] = gray_boss_skull
return is_dead
def _calculate_reward(self):
reward_movement = 0
reward_projectile = 0
reward_movement += (-1 if self.game_state["health"][0] <
self.game_state["health"][1] else 0.05)
reward_projectile += (1 if self.game_state["boss_health"][0] <
self.game_state["boss_health"][3] else -0.05)
return reward_movement, reward_projectile
class SerpentVVVVVVGameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.game_state = None
self._reset_game_state()
def setup_play(self):
input_mapping = {
#"UP": [KeyboardKey.KEY_UP],
"LEFT": [KeyboardKey.KEY_LEFT],
#"DOWN": [KeyboardKey.KEY_DOWN],
"RIGHT": [KeyboardKey.KEY_RIGHT]
}
self.key_mapping = {
#KeyboardKey.KEY_UP.name: "UP",
KeyboardKey.KEY_LEFT.name: "LEFT",
#KeyboardKey.KEY_DOWN.name: "DOWN",
KeyboardKey.KEY_RIGHT.name: "RIGHT"
}
direction_action_space = KeyboardMouseActionSpace(
direction_keys=[None, "LEFT", "RIGHT"])
direction_model_file_path = "datasets/vvvvvv_direction_dqn_0_1_.h5".replace(
"/", os.sep)
self.dqn_direction = DDQN(
model_file_path=direction_model_file_path
if os.path.isfile(direction_model_file_path) else None,
input_shape=(480, 640, 4),
input_mapping=input_mapping,
action_space=direction_action_space,
replay_memory_size=5000,
max_steps=1000000,
observe_steps=600,
batch_size=32,
model_learning_rate=1e-4,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=False)
def handle_play(self, game_frame):
gc.disable()
for i, game_frame in enumerate(self.game_frame_buffer.frames):
self.visual_debugger.store_image_data(game_frame.frame,
game_frame.frame.shape,
str(i))
if self.dqn_direction.first_run:
#self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
#time.sleep(5)
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
self.dqn_direction.first_run = False
time.sleep(5)
return None
actor_hp = self._measure_actor_hp(game_frame)
run_score = self._measure_run_score(game_frame)
self.game_state["health"].appendleft(actor_hp)
self.game_state["score"].appendleft(run_score)
if self.dqn_direction.frame_stack is None:
full_game_frame = FrameGrabber.get_frames(
[0],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE").frames[0]
self.dqn_direction.build_frame_stack(full_game_frame.frame)
else:
game_frame_buffer = FrameGrabber.get_frames(
[0, 4, 8, 12],
frame_shape=(self.game.frame_height, self.game.frame_width),
frame_type="PIPELINE")
if self.dqn_direction.mode == "TRAIN":
reward_direction, reward_action = self._calculate_reward()
self.game_state["run_reward_direction"] += reward_direction
self.game_state["run_reward_action"] += reward_action
self.dqn_direction.append_to_replay_memory(
game_frame_buffer,
reward_direction,
terminal=self.game_state["health"] == 0)
# Every 2000 steps, save latest weights to disk
if self.dqn_direction.current_step % 2000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/vvvvvv_direction")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_direction.current_step % 20000 == 0:
self.dqn_direction.save_model_weights(
file_path_prefix=f"datasets/vvvvvv_direction",
is_checkpoint=True)
elif self.dqn_direction.mode == "RUN":
self.dqn_direction.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(
"GAME: VVVVVV PLATFORM: Steam AGENT: DDQN + Prioritized Experience Replay"
)
print("")
self.dqn_direction.output_step_data()
print(f"CURRENT RUN: {self.game_state['current_run']}")
print(
f"CURRENT RUN REWARD: {round(self.game_state['run_reward_direction'] + self.game_state['run_reward_action'], 2)}"
)
print(
f"CURRENT RUN PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(f"CURRENT HEALTH: {self.game_state['health'][0]}")
print(f"CURRENT SCORE: {self.game_state['score'][0]}")
print("")
print(
f"LAST RUN DURATION: {self.game_state['last_run_duration']} seconds"
)
print("")
print(
f"RECORD TIME ALIVE: {self.game_state['record_time_alive'].get('value')} seconds (Run {self.game_state['record_time_alive'].get('run')}, {'Predicted' if self.game_state['record_time_alive'].get('predicted') else 'Training'})"
)
print("")
print(
f"RANDOM AVERAGE TIME ALIVE: {self.game_state['random_time_alive']} seconds"
)
if self.game_state["health"][1] <= 0:
serpent.utilities.clear_terminal()
timestamp = datetime.utcnow()
gc.enable()
gc.collect()
gc.disable()
timestamp_delta = timestamp - self.game_state["run_timestamp"]
self.game_state["last_run_duration"] = timestamp_delta.seconds
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
# Check for Records
if self.game_state["last_run_duration"] > self.game_state[
"record_time_alive"].get("value", 0):
self.game_state["record_time_alive"] = {
"value": self.game_state["last_run_duration"],
"run": self.game_state["current_run"],
"predicted": self.dqn_direction.mode == "RUN"
}
else:
self.game_state["random_time_alives"].append(
self.game_state["last_run_duration"])
self.game_state["random_time_alive"] = np.mean(
self.game_state["random_time_alives"])
self.game_state["current_run_steps"] = 0
self.input_controller.handle_keys([])
if self.dqn_direction.mode == "TRAIN":
for i in range(8):
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print(
f"SESSION RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} seconds"
)
print(
"GAME: VVVVVV PLATFORM: Steam AGENT: DDQN + Prioritized Experience Replay"
)
print("")
print(f"TRAINING ON MINI-BATCHES: {i + 1}/2")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 20 == 0 else ''}"
)
self.dqn_direction.train_on_mini_batch()
self.game_state["run_timestamp"] = datetime.utcnow()
self.game_state["current_run"] += 1
self.game_state["run_reward_direction"] = 0
self.game_state["run_reward_action"] = 0
self.game_state["run_predicted_actions"] = 0
self.game_state["health"] = collections.deque(np.full((8, ),
3),
maxlen=8)
self.game_state["score"] = collections.deque(np.full((8, ), 0),
maxlen=8)
if self.dqn_direction.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
if self.dqn_direction.type == "DDQN":
self.dqn_direction.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_direction.enter_run_mode()
else:
self.dqn_direction.enter_train_mode()
#self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
#time.sleep(3)
self.input_controller.tap_key(KeyboardKey.KEY_SPACE)
return None
self.dqn_direction.pick_action()
self.dqn_direction.generate_action()
keys = self.dqn_direction.get_input_values()
print("")
print("PRESSING: ", end='')
print(" + ".join(
list(map(lambda k: self.key_mapping.get(k.name), keys))))
self.input_controller.handle_keys(keys)
if self.dqn_direction.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_direction.erode_epsilon(factor=2)
self.dqn_direction.next_step()
self.game_state["current_run_steps"] += 1
def _reset_game_state(self):
self.game_state = {
"health": collections.deque(np.full((8, ), 3), maxlen=8),
"score": collections.deque(np.full((8, ), 0), maxlen=8),
"run_reward_direction": 0,
"run_reward_action": 0,
"current_run": 1,
"current_run_steps": 0,
"current_run_health": 0,
"current_run_score": 0,
"run_predicted_actions": 0,
"last_run_duration": 0,
"record_time_alive": dict(),
"random_time_alive": None,
"random_time_alives": list(),
"run_timestamp": datetime.utcnow(),
}
def _measure_actor_hp(self, game_frame):
hp_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["HP_AREA"])
hp_area_image = Image.fromarray(hp_area_frame)
actor_hp = 0
image_colors = hp_area_image.getcolors(
) # TODO: remove in favor of sprite detection and location
if image_colors:
actor_hp = len(image_colors) - 7
for name, sprite in self.game.sprites.items():
query_sprite = Sprite("QUERY", image_data=sprite.image_data)
sprite_name = self.sprite_identifier.identify(
query_sprite, mode="CONSTELLATION_OF_PIXELS"
) # Will be "UNKNOWN" if no match
print(sprite_name)
sprite_to_locate = Sprite("QUERY", image_data=sprite.image_data)
sprite_locator = SpriteLocator()
location = sprite_locator.locate(sprite=sprite_to_locate,
game_frame=game_frame)
print(location)
if location:
actor_hp = 1000000
return actor_hp
def _measure_run_score(self, game_frame):
score_area_frame = serpent.cv.extract_region_from_image(
game_frame.frame, self.game.screen_regions["SCORE_AREA"])
score_grayscale = np.array(skimage.color.rgb2gray(score_area_frame) *
255,
dtype="uint8")
score_image = Image.fromarray(score_grayscale)
score = '0'
image_colors = score_image.getcolors()
if image_colors and len(image_colors) > 1:
score = serpent.ocr.perform_ocr(image=score_grayscale,
scale=10,
order=5,
horizontal_closing=10,
vertical_closing=5)
score = score.split(":")[0]
count = 0
if not score.isdigit():
score = '0'
self.game_state["current_run_score"] = score
return score
def _calculate_reward(self):
reward = 0
reward += self.game_state["health"][0] / 10.0
reward += (0.5 if (int(self.game_state["score"][0]) -
int(self.game_state["score"][1])) >= 0 else -0.25)
return reward, reward
class SerpentPika2GameAgent(GameAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.frame_handlers["PLAY"] = self.handle_play
self.frame_handler_setups["PLAY"] = self.setup_play
self.previous_game_frame = None
self.lowerY = np.array([255, 255, 0], np.uint8)
self.upperY = np.array([255, 255, 10], np.uint8)
self.lowerR = np.array([255, 0, 0], np.uint8)
self.upperR = np.array([255, 0, 10], np.uint8)
self.game_state = None
self._reset_game_state()
def setup_key(self):
self.input_mapping = {
"JUMP": [KeyboardKey.KEY_UP],
"RIGHT": [KeyboardKey.KEY_RIGHT],
"LEFT": [KeyboardKey.KEY_LEFT],
"LEFT JUMP": [KeyboardKey.KEY_LEFT, KeyboardKey.KEY_UP],
"RIGHT JUMP": [KeyboardKey.KEY_RIGHT, KeyboardKey.KEY_UP],
"HIT": [KeyboardKey.KEY_RETURN],
"None": []
}
self.key_mapping = {
KeyboardKey.KEY_UP: "UP",
KeyboardKey.KEY_RIGHT: "RIGHT",
KeyboardKey.KEY_DOWN: "DOWN",
KeyboardKey.KEY_LEFT: "LEFT",
KeyboardKey.KEY_RETURN: "HIT"
}
self.action_space = KeyboardMouseActionSpace(action=['None', 'HIT'])
self.move_action_space = KeyboardMouseActionSpace(
action=['None', 'JUMP', 'RIGHT', 'LEFT'])
'''
move_inputs = {
"JUMP": [KeyboardKey.KEY_UP],
"RIGHT": [KeyboardKey.KEY_RIGHT],
"LEFT": [KeyboardKey.KEY_LEFT],
"NO_MOVE": []
}
attack_inputs = {
"Power Hit": [KeyboardKey.KEY_RETURN],
"NO_HIT": []
}
self.game_inputs = dict()
for move_label, attack_label in itertools.product(move_inputs, attack_inputs):
label = f"{move_label.ljust(10)}{attack_label}"
self.game_inputs[label] = move_inputs[move_label] + attack_inputs[attack_label]
print(self.game_inputs)
'''
def setup_play(self):
#self.cid = 0
self.trainID = 0
self.setup_key()
self.frame_process = False
self.rewards = list()
self.started_at = datetime.now()
self.started_at_str = self.started_at.isoformat()
self.save_point_path = 'score.npy'
if os.path.isfile(self.save_point_path):
self.score_record = np.load(self.save_point_path)
else:
self.score_record = np.zeros(shape=(0, ))
self.collision_count_path = 'collision.npy'
self.reward_sum = 0
if os.path.isfile(self.collision_count_path):
self.collision_counter = np.load(self.collision_count_path)
else:
self.collision_counter = np.zeros(shape=(0, ))
latest_epsilon = 1
action_model_path = 'dqn_action_0_1_.h5'
model_list = os.listdir('model/action')
for item in model_list:
for epsilon in re.findall("\d+\.\d+", item):
if latest_epsilon > float(epsilon):
latest_epsilon = float(epsilon)
action_model_path = item
action_model_path = f'model/action/{action_model_path}'.replace(
'/', os.sep)
self.dqn_action = DDQN(
model_file_path=action_model_path
if os.path.isfile(action_model_path) else None,
input_shape=(100, 100, 4),
input_mapping=self.input_mapping,
action_space=self.action_space,
replay_memory_size=5000,
max_steps=2000000,
observe_steps=100 if os.path.isfile(action_model_path) else 2000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=True)
latest_epsilon = 1
move_model_path = 'dqn_move_0_1_.h5'
model_list = os.listdir('model/move')
for item in model_list:
for epsilon in re.findall("\d+\.\d+", item):
if latest_epsilon > float(epsilon):
latest_epsilon = float(epsilon)
move_model_path = item
move_model_path = f'model/move/{move_model_path}'.replace('/', os.sep)
self.dqn_move = DDQN(
model_file_path=move_model_path
if os.path.isfile(move_model_path) else None,
input_shape=(100, 100, 4),
input_mapping=self.input_mapping,
action_space=self.move_action_space,
replay_memory_size=5000,
max_steps=2000000,
observe_steps=100 if os.path.isfile(move_model_path) else 2000,
batch_size=32,
initial_epsilon=1,
final_epsilon=0.01,
override_epsilon=True)
print('Starting Game')
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
def getDifference(self, game_frame, previous_game_frame):
return game_frame.grayscale_frame - previous_game_frame.grayscale_frame
def handle_play(self, game_frame):
# append memory data into game state
(self.game_state["com_x"], self.game_state["com_y"], self.ai_x,
self.ai_y, self.ball_x, self.ball_y, self.com_sc, self.ai_sc,
self.col_size, self.game_state["col_x"],
self.game_state["col_y"]) = readInfo()
self.game_state["ai_x"].appendleft(self.ai_x)
self.game_state["ai_y"].appendleft(self.ai_y)
self.game_state["ball_x"].appendleft(self.ball_x)
self.game_state["ball_y"].appendleft(self.ball_y)
self.game_state["ai_score"].appendleft(self.ai_sc)
self.game_state["com_score"].appendleft(self.com_sc)
self.game_state["col_size"].appendleft(self.col_size)
# judge is-in-game by read pixel value (tricky)
self.game_frame_img = FrameGrabber.get_frames(
[0], frame_type="PIPELINE").frames[0].frame
if self.game_frame_img[91, 49] != 0.3607843137254902:
self.handle_notInGame()
else:
self.game_state["playing"] = True
self.handle_fight(game_frame)
def handle_notInGame(self):
serpent.utilities.clear_terminal()
print('Currently not in game...please wait..')
playAnimation(self.game_state["animeIndex"])
self.game_state["animeIndex"] = self.game_state[
"animeIndex"] + 1 if self.game_state["animeIndex"] < 3 else 0
#print(self.game_frame_img[75:97,47:52])
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
time.sleep(0.3)
def handle_fight(self, game_frame):
gc.disable()
if self.dqn_action.first_run:
self.dqn_action.first_run = False
self.dqn_move.first_run = False
return
if self.dqn_action.frame_stack is None:
game_frame_buffer = FrameGrabber.get_frames(
[0], frame_type="PIPELINE").frames[0]
self.dqn_action.build_frame_stack(game_frame_buffer.frame)
self.dqn_move.frame_stack = self.dqn_action.frame_stack
else:
game_frame_buffer = FrameGrabber.get_frames([0, 1, 2, 3],
frame_type="PIPELINE")
if self.dqn_action.mode == "TRAIN":
reward = self._calculate_reward()
self.game_state["reward"] = reward
self.dqn_action.append_to_replay_memory(
game_frame_buffer,
reward,
terminal=self.game_state["ai_score"][0] == 15)
self.dqn_move.append_to_replay_memory(
game_frame_buffer,
reward,
terminal=self.game_state["ai_score"][0] == 15)
# Every 2000 steps, save latest weights to disk
if self.dqn_action.current_step % 1000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"model/action/dqn_action")
self.dqn_move.save_model_weights(
file_path_prefix=f"model/move/dqn_move")
# Every 20000 steps, save weights checkpoint to disk
if self.dqn_action.current_step % 10000 == 0:
self.dqn_action.save_model_weights(
file_path_prefix=f"model/action/dqn_action",
is_checkpoint=True)
self.dqn_move.save_model_weights(
file_path_prefix=f"model/move/dqn_move",
is_checkpoint=True)
elif self.dqn_action.mode == "RUN":
self.dqn_action.update_frame_stack(game_frame_buffer)
self.dqn_move.update_frame_stack(game_frame_buffer)
run_time = datetime.now() - self.started_at
serpent.utilities.clear_terminal()
print('')
print(Fore.YELLOW)
print(Style.BRIGHT)
print(f"STARTED AT:{self.started_at_str}")
print(
f"RUN TIME: {run_time.days} days, {run_time.seconds // 3600} hours, {(run_time.seconds // 60) % 60} minutes, {run_time.seconds % 60} s"
)
print(Style.RESET_ALL)
#print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print("MOVEMENT NEURAL NETWORK:\n")
self.dqn_move.output_step_data()
print("")
print("ACTION NEURAL NETWORK:\n")
self.dqn_action.output_step_data()
print(Style.RESET_ALL)
print(Style.BRIGHT)
print(f"CURRENT RUN: {self.game_state['current_run'] }")
print("")
print(
f"CURRENT RUN REWARD: {round(self.game_state['reward'], 4)}")
print(f"CURRENT AI SCORE: {self.game_state['ai_score'][0]}")
print(f"CURRENT ENEMY SCORE: {self.game_state['com_score'][0]}")
print("")
print(
f"PREDICTED ACTIONS: {self.game_state['run_predicted_actions']}"
)
print(Style.RESET_ALL)
self.dqn_action.pick_action()
#self.dqn_action.pick_action(action_type="PREDICTED")
self.dqn_action.generate_action()
self.dqn_move.pick_action(
action_type=self.dqn_action.current_action_type)
#self.dqn_move.pick_action(action_type="PREDICTED")
self.dqn_move.generate_action()
movement_keys = self.dqn_move.get_input_values(
) + self.dqn_action.get_input_values()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
#print(movement_keys)
#print(" + ".join(list(map(lambda k: self.key_mapping.get(k), movement_keys))))
print(" + ".join(
list(
map(lambda k: self.key_mapping.get(k).ljust(5),
movement_keys))))
print(Style.RESET_ALL)
print("")
print(
f"AI: ({self.game_state['ai_x'][0]}, {self.game_state['ai_y'][0]})"
)
print(
f"COM: ({self.game_state['com_x']}, {self.game_state['com_y']})"
)
print(
f"BALL: ({self.game_state['ball_x'][0]}, {self.game_state['ball_y'][0]})"
)
print(
f"Collision: ({self.game_state['col_x']}, {self.game_state['col_y']}, {self.game_state['col_size'][0]})"
)
print(f"Distance: {self.game_state['distance'][0]}")
self.input_controller.handle_keys(movement_keys)
if self.dqn_action.current_action_type == "PREDICTED":
self.game_state["run_predicted_actions"] += 1
self.dqn_action.erode_epsilon(factor=2)
self.dqn_move.erode_epsilon(factor=2)
self.dqn_action.next_step()
self.dqn_move.next_step()
self.game_state["current_run"] += 1
if self.game_state['ai_score'][0] == 15 or self.game_state[
'com_score'][0] == 15:
# Game over
self.game_state["ai_score"].appendleft(0)
self.game_state["com_score"].appendleft(0)
self.score_record = np.append(self.score_record,
self.game_state['ai_score'][1])
np.save(self.save_point_path, self.score_record)
self.handle_fight_end(game_frame)
def handle_fight_end(self, game_frame):
self.game_state["playing"] = False
self.input_controller.handle_keys([])
self.game_state["current_run"] += 1
self.handle_fight_training(game_frame)
def handle_fight_training(self, game_frame):
#self.input_controller.tap_key(KeyboardKey.KEY_ESCAPE)
serpent.utilities.clear_terminal()
gc.enable()
gc.collect()
gc.disable()
print("TRAIN MODE")
self.input_controller.handle_keys([])
if self.dqn_action.mode == "TRAIN":
for i in range(16):
serpent.utilities.clear_terminal()
print("")
print(Fore.GREEN)
print(Style.BRIGHT)
print(f"TRAINING ON MINI-BATCHES: {i + 1}/16")
print(
f"NEXT RUN: {self.game_state['current_run'] + 1} {'- AI RUN' if (self.game_state['current_run'] + 1) % 25 == 0 else ''}"
)
print(Style.RESET_ALL)
self.dqn_action.train_on_mini_batch()
self.dqn_move.train_on_mini_batch()
self.game_state["run_predicted_actions"] = 0
if self.dqn_action.mode in ["TRAIN", "RUN"]:
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 100 == 0:
self.dqn_action.update_target_model()
self.dqn_move.update_target_model()
if self.game_state["current_run"] > 0 and self.game_state[
"current_run"] % 20 == 0:
self.dqn_action.enter_run_mode()
self.dqn_move.enter_run_mode()
else:
self.dqn_action.enter_train_mode()
self.dqn_move.enter_train_mode()
self.input_controller.tap_key(KeyboardKey.KEY_RETURN)
time.sleep(2)
def _reset_game_state(self):
self.game_state = {
"reward": 0,
"animeIndex": 0,
"current_run": 1,
"playing": False,
"run_predicted_actions": 0,
"ai_x": collections.deque(np.full((4, ), 0), maxlen=4),
"ai_y": collections.deque(np.full((4, ), 0), maxlen=4),
"ai_score": collections.deque(np.full((4, ), 0), maxlen=4),
"ball_x": collections.deque(np.full((4, ), 0), maxlen=4),
"ball_y": collections.deque(np.full((4, ), 0), maxlen=4),
"com_score": collections.deque(np.full((4, ), 0), maxlen=4),
"col_size": collections.deque(np.full((4, ), 6), maxlen=4),
"com_x": 36,
"com_y": 244,
"col_x": 0,
"col_y": 0,
"distance": collections.deque(np.full((20, ), 100), maxlen=20),
}
def _calculate_reward(self):
reward = 0
distance = math.sqrt(
abs(self.game_state["ai_x"][0] - self.game_state["ball_x"][0])**2 +
abs(self.game_state["ai_y"][0] - self.game_state["ball_y"][0])**2)
self.game_state["distance"].appendleft(int(distance))
# to make ai move lesser
#if self.game_state["ai_x"][0] == self.game_state["ai_x"][1]:
# reward += 0.1
# collision with ball
collision = self.game_state["distance"][0] < 80 and self.game_state[
"distance"][1] < 80 and self.game_state["distance"][
2] < 80 and self.game_state["distance"][0] > self.game_state[
"distance"][1] and self.game_state["distance"][
1] < self.game_state["distance"][2]
if collision:
reward += 0.25
# power hit
if self.game_state["col_size"][0] > 0 and self.game_state["distance"][
0] < 90 and self.game_state["col_y"] != 272:
reward += 0.5
# AI gain score
if self.game_state["ai_score"][0] > self.game_state["ai_score"][1]:
reward += 1
self.collision_counter = np.append(self.collision_counter,
self.reward_sum)
np.save(self.collision_count_path, self.collision_counter)
self.reward_sum = 0
# Com gain score
if self.game_state["com_score"][0] > self.game_state["com_score"][1]:
reward += -1
self.collision_counter = np.append(self.collision_counter,
self.reward_sum)
np.save(self.collision_count_path, self.collision_counter)
self.reward_sum = 0
if reward > 1:
reward = 1
self.game_state["reward"] = reward
self.reward_sum += reward
return reward
