class Agent(AbstractPlayer):
def __init__(self):
AbstractPlayer.__init__(self)
self.lastSsoType = LEARNING_SSO_TYPE.BOTH
self.episodes = 0
self.brain = None
def init(self, sso, elapsedTimer):
pass
"""
* Method used to determine the next move to be performed by the agent.
* This method can be used to identify the current state of the game and all
* relevant details, then to choose the desired course of action.
*
* @param sso Observation of the current state of the game to be used in deciding
* the next action to be taken by the agent.
* @param elapsedTimer Timer (40ms)
* @return The action to be performed by the agent.
"""
def act(self, sso: 'SerializableStateObservation', elapsedTimer):
# Start the model
if self.brain is None:
self.brain = Brain(self.get_state().shape, sso.availableActions)
self.brain.load_model()
# Simple forward pass to get action
action = self.brain.get_action(self.get_state(), sso.availableActions)
return action
"""
* Method used to perform actions in case of a game end.
* This is the last thing called when a level is played (the game is already in a terminal state).
* Use this for actions such as teardown or process data.
*
* @param sso The current state observation of the game.
* @param elapsedTimer Timer (up to CompetitionParameters.TOTAL_LEARNING_TIME
* or CompetitionParameters.EXTRA_LEARNING_TIME if current global time is beyond TOTAL_LEARNING_TIME)
* @return The next level of the current game to be played.
* The level is bound in the range of [0,2]. If the input is any different, then the level
* chosen will be ignored, and the game will play a random one instead.
"""
def result(self, sso, elapsedTimer):
self.episodes += 1
win = "True " if "WIN" in sso.gameWinner else "False"
print("{}. Win: {} | Game Ticks: {} | Epsilon: {:.3f}".format(self.episodes,
win,
sso.gameTick,
self.brain.exploration_rate))
return random.randint(0, 2)
def get_state(self):
image = cv2.imread(CompetitionParameters.SCREENSHOT_FILENAME, cv2.IMREAD_COLOR)
resized_image = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
return np.array(resized_image)
import cv2
movements = [
NEW_COMPLEX_MOVEMENT, COMPLEX_MOVEMENT, SIMPLE_MOVEMENT, RIGHT_ONLY
]
movement = movements[3]
num_movement = len(movement)
load = False
brain = Brain()
brain.action_list = movement
if not load:
brain.create_brain_q((60, 64))
else:
brain.load_model(
'model_fitness_4208_generation_99_population_index_99.sav',
os.getcwd() + "/models/")
brain.generation_num = 99
brain.index = 99
fitness_list = []
environments_mario = [
"SuperMarioBros-v0", "SuperMarioBros-v1", "SuperMarioBros-v2",
"SuperMarioBros-v3", "SuperMarioBrosNoFrameskip-v0",
"SuperMarioBrosNoFrameskip-v1", "SuperMarioBrosNoFrameskip-v2",
"SuperMarioBrosNoFrameskip-v3", "SuperMarioBros2-v0", "SuperMarioBros2-v1",
"SuperMarioBros2NoFrameskip-v0", "SuperMarioBros2NoFrameskip-v1"
]
def create_environment(_env, movement_set):
