def init_bots(self):
self.bots = [Bot1(2), Bot2(3), Bot4(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
for (bot) in self.bots: # at the beginning, all bots will have same position, energy as player
bot.info.posx = self.user.posx
bot.info.posy = self.user.posy
bot.info.energy = self.user.energy
bot.info.lastAction = -1
bot.info.status = PlayerInfo.STATUS_PLAYING
bot.info.score = 0
self.stepState.players.append(bot.info)
self.userMatch.gameinfo.numberOfPlayers = len(self.stepState.players)
print("numberOfPlayers: ", self.userMatch.gameinfo.numberOfPlayers)
def __init__(self):
pygame.init()
self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption("Future")
self.clock = pygame.time.Clock()
self.a_map = World_map()
self.bender = Robot(self)
self.mouse_pos_x, self.mouse_pos_y = 0, 0
self.bullets = pygame.sprite.Group()
self.bots1 = pygame.sprite.Group()
bot1 = Bot1(self)
bot1.rect.x = 904
bot1.rect.y = 282
bot1.real_x = 904
bot1.real_y = 282
bot1.x_on_a_map = 904
bot1.y_on_a_map = 282
self.bots1.add(bot1)
bot1 = Bot1(self)
bot1.rect.x = 104
bot1.rect.y = 292
bot1.real_x = 104
bot1.real_y = 292
bot1.x_on_a_map = 104
bot1.y_on_a_map = 292
self.bots1.add(bot1)
self.bots2 = pygame.sprite.Group()
self.bot2 = Bot2(self)
self.bot2.rect.x = 600
self.bot2.rect.y = 92
self.bots2.add(self.bot2)
self.gun1 = Guns(self)
self.fire = False
self.gun1_status = False
self.move_left = False
self.move_right = False
self.camera = Camera(self.camera_configure, self)
def init_bots(self):
#self.bots = [Bot1(2), Bot2(3), Bot3(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
tmp_random = randrange(20)
if tmp_random == 0 or tmp_random > 15:
self.bots = [Bot5(2), Bot6(3), Bot2(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random > 9:
self.bots = [Bot2(2), Bot5(3), Bot6(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 1:
self.bots = [Bot1(2), Bot2(3), Bot6(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 2:
self.bots = [Bot5(2), Bot1(3), Bot2(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 3:
self.bots = [Bot6(2), Bot5(3), Bot1(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 4:
self.bots = [Bot3(2), Bot6(3), Bot2(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 5:
self.bots = [Bot2(2), Bot3(3), Bot5(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 6:
self.bots = [Bot5(2), Bot6(3), Bot3(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 7:
self.bots = [Bot6(2), Bot2(3), Bot4(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
elif tmp_random == 8:
self.bots = [Bot4(2), Bot2(3), Bot5(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
else:
self.bots = [Bot6(2), Bot5(3), Bot4(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
#self.bots = [Bot5(2), Bot6(3), Bot2(4)] # use bot1(id=2), bot2(id=3), bot3(id=4)
for (bot) in self.bots: # at the beginning, all bots will have same position, energy as player
bot.info.posx = self.user.posx
bot.info.posy = self.user.posy
bot.info.energy = self.user.energy
bot.info.lastAction = -1
bot.info.status = PlayerInfo.STATUS_PLAYING
bot.info.score = 0
self.stepState.players.append(bot.info)
self.userMatch.gameinfo.numberOfPlayers = len(self.stepState.players)
print("numberOfPlayers: ", self.userMatch.gameinfo.numberOfPlayers)
# Parameters for training a DQN model
N_EPISODE = 100000 #The number of episodes for training
MAX_STEP = 1000 #The number of steps for each episode
BATCH_SIZE = 32 #The number of experiences for each replay
MEMORY_SIZE = 100000 #The size of the batch for storing experiences
SAVE_NETWORK = 1000 # After this number of episodes, the DQN model is saved for testing later.
INITIAL_REPLAY_SIZE = 10000 #The number of experiences are stored in the memory batch before starting replaying
INPUTNUM = (2*limit+1)**2 + 3#198 #The number of input values for the DQN model
ACTIONNUM = 6 #The number of actions output from the DQN model
MAP_MAX_X = 21 #Width of the Map
MAP_MAX_Y = 9 #Height of the Map
# Initialize a DQN model and a memory batch for storing experiences
DQNAgent = DQN(INPUTNUM, ACTIONNUM)
memory = Memory(MEMORY_SIZE)
bots = [Bot1(2), Bot2(3), Bot3(4)]
#load model to continue training
if args.load_model !="":
file_name = "TrainedModels/DQNmodel_20200730-1832_ep1000out-30.json"
json_file = file_name if args.load_model == "default" else args.load_model
DQNAgent.load_model(json_file)
# Initialize environment
minerEnv = MinerEnv(HOST, PORT) #Creating a communication environment between the DQN model and the game environment (GAME_SOCKET_DUMMY.py)
minerEnv.start() # Connect to the game
train = False #The variable is used to indicate that the replay starts, and the epsilon starts decrease.
#Training Process
#the main part of the deep-q learning agorithm
for episode_i in range(0, N_EPISODE):
print(np.unique(DQNAgent.model.get_weights()[0]))