def runGame(p1, p2):
p1.resetForNewGame()
p2.resetForNewGame()
d1 = Dealer(5)
p1.cards = d1.dealCards()[0]
p2.cards = d1.dealCards()[0]
gameState = GameState(p1, p2)
aqlearnScore = 0
gameLoop = True
while gameLoop:
p1.pickCard()
p2.pickCard()
roundWinner = gameState.judgeRound(p1, p2)
#? generating successor
if roundWinner == p1:
p1.accumulatedCards[p1.playedCard[1]] += 1
if roundWinner == p2:
p2.accumulatedCards[p2.playedCard[1]] += 1
if gameState.judgeGameOver(p1, p2) == p1:
return p1.name
elif gameState.judgeGameOver(p1, p2) == p2:
return p2.name
p1.cards.append(d1.generateRandomCard())
p2.cards.append(d1.generateRandomCard())
def main():
#初始化pygame
pygame.init()
#创建屏幕对象
#设置分辨率
screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
#设置窗口
pygame.display.set_caption("俄罗斯方块")
#让按键看起来更连续一些
pygame.key.set_repeat(10, 100)
#背景颜色RGB
bg_color = BACKGROUND_COLOR
random.seed(int(time.time()))
game_state = GameState(screen)
game_resource = GameResource()
#游戏主循环
while True:
#判断方块是否落在最底部
if game_state.piece and game_state.piece.is_on_bottom:
game_state.wall.add_to_wall(game_state.piece)
game_state.add_score(game_state.wall.eliminate_lines())
game_state.piece = Piece(random.choice(PIECE_TYPES), screen,
game_state.wall)
check_events(game_state)
#设置屏幕背景颜色
screen.fill(bg_color)
#绘制游戏区域，网格线和墙体
GameDisplay.draw_game_area(screen, game_state, game_resource)
if game_state.piece:
game_state.piece.paint()
#刷新屏幕
pygame.display.flip()
def runGame(searchAlgo, initialGameState):
# Create a current state object and initialize it for given input
currentState = GameState()
currentState.initializeGame(initialGameState)
printPuzzle(currentState) # To view whats going on
# Send the initial state to the SearchAgent along with searchType
searchType = SearchAgent(searchAlgo)
searchType.registerInitialState(currentState)
searchType.generateOutFile()
def __init__(self):
'# Initialize game window and settings etc.'
self.screen = pygame.display.set_mode((1000, 1000))
pygame.display.set_caption("Asteroids")
pygame.font.init()
self.myfont = pygame.font.SysFont('Comic Sans MS', 30)
self.debug_text_surface = self.myfont.render('Hello', False,
(255, 255, 255))
self.render_pace: float = 1 / 60
self.game_active = True
asteroid_list = []
bullet_list = []
ship = Ship(self.screen, Point(400, 500), Point(0, 0), -math.pi / 2, 1)
enemy_ship = Ship(self.screen, Point(600, 500), Point(0, 0),
-math.pi / 2, 1)
self.game_state = GameState(ship, enemy_ship, bullet_list,
asteroid_list)
for count in range(0, 8):
asteroids = Asteroid(self.screen,
Point(randint(0, 900), randint(0, 900)),
Point(randint(-20, 20), randint(-20, 20)),
randint(120, 170))
list.append(asteroid_list, asteroids)
self.movement_manager = MovementManager(self.render_pace, 1000, 1000)
self.collision_manager = CollisionManager()
def turn(self):
activeAgent = self.activeFighter
options = activeAgent.getAttackOptions()
decision = self.controller.decide(options)
effect = activeAgent.getAttackEffects(decision)
receptiveAgent = GameState.getEnemyActive(self.name)
receptiveAgent.receiveEffects(effect)
def calculate_movement(self, game_state: GameState) -> GameState:
"""Calculates movement for all game objects in the game state"""
'# Calculate ships movements'
game_state.my_ship = self.calculate_ship_movement(game_state.my_ship)
game_state.enemy_ship = self.calculate_ship_movement(
game_state.enemy_ship)
'# Calculate movement for asteroids and for bullets'
for i in game_state.asteroids:
i.pos += i.pos_delta * self.renderPace
i.pos = self.calculate_wrap(i.pos)
for i in game_state.bullets:
i.pos += i.pos_delta * self.renderPace
i.pos = self.calculate_wrap(i.pos)
return game_state
def _check_winner(state: GameState) -> bool:
return _all_same(state.get_first_column()) or \
_all_same(state.get_first_row()) or \
_all_same(state.get_second_column()) or \
_all_same(state.get_second_row()) or \
_all_same(state.get_third_column()) or \
_all_same(state.get_third_row()) or \
_all_same(state.get_top_left_diagonal()) or \
_all_same(state.get_top_right_diagonal())
def run(self,
num_plays=PLAYS_PER_EVAL,
search_iters=EVAL_PLAY_SEARCH_ITERS,
markov_exp=EVAL_PLAY_MARKOV_EXP):
'''Starts an evaluation. The evaluation process is synchronized
with the self-play processes and evaluation processes via instances of
UpdateSignal and AsyncSignal, respectively, in the main script:
asyn_training.py. The UpdateSignal triggers an update in each of the
self-play processes if the total number of apprentice wins to total
evaluation games surpasses the declared win ratio while the AsyncSignal
triggers the evaluation processes.
Parameters
----------
num_plays : int (positive), optional
The numnber of evaluation games to play. The default is
PLAYS_PER_EVAL.
search_iters : int (positive), optional
the number of search iterations to perform during MCTS.
The default is EVAL_PLAY_SEARCH_ITERS.
markov_exp : float, optional
The exponent determining the number of steps taken in
the markov chain in generating games for evaluation.
Returns
-------
apprentice_wins : int (nonegative)
the number of apprentice wins.
'''
# put models in eval mode...
self.alpha_agent.model.eval()
self.apprentice_agent.model.eval()
# setup gameplay
alpha = 0
apprentice = 1
actions = np.arange(MAX_NODES)
state_generator = GameState.state_generator(markov_exp)
apprentice_wins = 0
# start evaluation game play
for i in range(num_plays):
# uniformly randomly choose which agent plays first
next_move = np.random.choice([alpha, apprentice])
# play a randomly generated game of upset-downset
game_state = next(state_generator)
while not game_state.is_terminal_state():
root = PUCTNode(game_state)
policy = self.alpha_agent.MCTS(root, search_iters, 0) \
if next_move == alpha \
else self.apprentice_agent.MCTS(root, search_iters, 0)
move = np.random.choice(actions, p=policy)
game_state = root.edges[move].state
next_move = 1 - next_move
# decide winner
winner = 1 - next_move
if winner == apprentice:
apprentice_wins += 1
return apprentice_wins
def check_winner(state: GameState) -> int or None:
global winner
if _check_winner(state):
return winner
elif len(state.get_possible_moves()) == 0:
winner = TIE
return TIE
return NO_PLAYER
class Controller:
def __init__(self):
self.board = Board()
self.game_state = GameState()
self.selected_square = [0, 0]
self.possible_moves = []
self.white_turn = True
def select_square(self, square):
self.selected_square = square
self.game_state.select_piece(self.selected_square)
self.possible_moves = self.game_state.get_moves(self.white_turn)
def handle_mouseclick(self):
mouse_state = pygame.mouse.get_pressed()
if mouse_state[0] == True:
mouse_pos = pygame.mouse.get_pos()
pressed_square = self.board.get_square(mouse_pos)
if self.is_possible_move(pressed_square):
self.game_state.move_selected_piece(pressed_square)
self.white_turn = not self.white_turn
self.select_square(pressed_square)
def is_possible_move(self, square):
if self.possible_moves == None:
return False
else:
for move in self.possible_moves:
if square[0] == move[0] and square[1] == move[1]:
return True
return False
def draw(self, surface):
self.board.draw(surface, self.selected_square, self.possible_moves)
self.game_state.draw_pieces(surface)
class Game:
def __init__(self):
pygame.display.set_caption("My Game")
self.FPS = 60
self.WIDTH = 900
self.HEIGHT = 500
self.WIN = pygame.display.set_mode((self.WIDTH, self.HEIGHT))
self.BLACK = (0, 0, 0)
self.WHITE = (255, 255, 255)
self.dest = (100, 100)
self.handler = Handler(self)
# should be before any other object that have handler in its constr.
# Handler(self) current instance in the current class
self.assets = Assets(self.handler)
self.gameState = GameState(self.handler)
self.gameState.init()
self.menuState = MenuState(self.handler)
self.menuState.init()
self.currentState = self.menuState
# self.ssTile = SsTiles('spritesheet.png')
# self.map = TileMap('test_level.csv', self.ssTile)
# self.bg = pygame.transform.rotate(self.bg, -90)
# draw everything here:
def draw_window(self):
self.WIN.fill(self.BLACK)
self.currentState.draw()
# end of drawing
pygame.display.flip()
def tick(self):
self.currentState.tick()
def run(self):
clock = pygame.time.Clock()
running = True
while running:
clock.tick(self.FPS)
self.handler.inputManager.tick()
self.tick()
self.draw_window()
def __init__(self, verbose, agentType, nHands, startingMoney, nTraining):
"""
Initialize the game! Create dealer and player objects and an initial gameState
input: verbose
whether or not to print each step as agents play
input: agentType
string representing the type of agent to instantiate
input: nHands
number of hands to play at max if agent never busts
input: startingMoney
the amount of money the agent gets to start with
input: nTraining
the number of training hands to do for a qlearning player
returns: nothing
"""
self.verbose = verbose
self.agentType = agentType
self.nHands = int(nHands) + int(nTraining)
self.nStartingHands = int(nHands) + int(nTraining)
print("{} test {} train {} total".format(nHands, nTraining,
self.nHands))
self.startingMoney = startingMoney
self.nTraining = int(nTraining)
self.dealer = Dealer()
self.player = self.createAgent(self.agentType, self.startingMoney,
nTraining)
self.agents = [self.player, self.dealer]
# Clean slate
dealerHand = Hand()
playerHand = Hand()
deck = Deck()
# list because player can split
playerHands = [playerHand]
if self.player:
initialBets = [self.player.getBetAmt()]
# Create initial game state
self.gameState = GameState(verbose, self.dealer, dealerHand,
self.player, playerHands, deck,
initialBets)
self.q = self.agentType == 'qlearning'
def __init__(self):
pygame.display.set_caption("My Game")
self.FPS = 60
self.WIDTH = 900
self.HEIGHT = 500
self.WIN = pygame.display.set_mode((self.WIDTH, self.HEIGHT))
self.BLACK = (0, 0, 0)
self.WHITE = (255, 255, 255)
self.dest = (100, 100)
self.handler = Handler(self)
# should be before any other object that have handler in its constr.
# Handler(self) current instance in the current class
self.assets = Assets(self.handler)
self.gameState = GameState(self.handler)
self.gameState.init()
self.menuState = MenuState(self.handler)
self.menuState.init()
self.currentState = self.menuState
def execute_one_game(self, pac_individual, ghost_individual):
"""
Execute one game / eval of a run given a Pac individual and
Ghost individual selected from their respective populations.
"""
# Pick a new map and set up a new game state.
game_map = self.experiment.pre_loaded_maps[random.randint(0, 99)]
self.experiment.world_data = []
game_state = GameState(game_map, self.experiment.pill_density,
self.experiment.time_multiplier,
self.experiment.fruit_spawning_probability,
self.experiment.fruit_score,
self.experiment.num_pacs,
self.experiment.num_ghosts)
game_state.write_world_config(self.experiment.world_data)
game_state.write_world_time_score(self.experiment.world_data)
# Create new Pac and Ghost controllers
for curr_pac_id in range(self.experiment.num_pacs):
self.pac_controllers[curr_pac_id] = PacController(
curr_pac_id, pac_individual)
for curr_ghost_id in range(self.experiment.num_ghosts):
self.ghost_controllers[curr_ghost_id] = GhostController(
curr_ghost_id, ghost_individual)
# While the game isn't over, play game turns.
game_over = False
while (not game_over):
game_over = game_state.play_turn(self.experiment.world_data,
self.pac_controllers,
self.ghost_controllers)
# Set Pac fitness and implement parsimony pressure
pac_individual.fitness = game_state.score
if (self.pac_pop.parsimony_technique == 'size'):
pac_individual.fitness -= (self.pac_pop.pppc *
pac_individual.root.size)
else:
pac_individual.fitness -= (self.pac_pop.pppc *
pac_individual.root.height)
# Set Ghost fitness and implement parsimony pressure
ghost_individual.fitness = -(game_state.score)
if (game_state.ghost_won):
ghost_individual.fitness += int(
(game_state.time * 100.0) / game_state.orig_time)
if (self.ghost_pop.parsimony_technique == 'size'):
ghost_individual.fitness -= (self.ghost_pop.pppc *
ghost_individual.root.size)
else:
ghost_individual.fitness -= (self.ghost_pop.pppc *
ghost_individual.root.height)
# Set Pac and Ghost scores
pac_individual.score = game_state.score # Score is raw game score without parsimony pressure for Pac
ghost_individual.score = ghost_individual.fitness # Score and fitness interchangeable for Ghost
def __init__(self, boardDim):
self.gameOffset = Point()
self.boardOffset = Point()
self.gemImgs = {}
self.gameState = GameState(boardDim)
self.calibrate()
self.submitImg = bitmap.Bitmap.open(SUBMIT_IMAGE_PATH)
self.replayImg = bitmap.Bitmap.open(REPLAY_IMAGE_PATH)
class Game:
def __init__(self):
pygame.display.set_caption("AREA CONQUEST")
self.FPS = 60
self.WIDTH = 900
self.HEIGHT = 500
self.WIN = pygame.display.set_mode((self.WIDTH, self.HEIGHT))
self.BLACK = (0, 0, 0)
self.WHITE = (255, 255, 255)
self.dest = (100, 100)
self.handler = Handler(self)
# should be before any other object that have handler in its constr.
# Handler(self) current instance in the current class
self.assets = Assets(self.handler)
self.menuState = MenuState(self.handler)
self.menuState.init()
self.gameState = GameState(self.handler)
self.gameState.init()
self.currentState = self.menuState
# draw everything here:
def draw_window(self):
self.WIN.fill(self.BLACK)
self.currentState.draw()
# end of drawing
pygame.display.flip()
def tick(self):
self.currentState.tick()
def run(self):
clock = pygame.time.Clock()
running = True
while running:
clock.tick(self.FPS)
self.handler.inputManager.tick()
self.tick()
self.draw_window()
def newGame(self,
layout,
pacmanType,
ghostType,
display,
agentOpts,
quiet=False,
catchExceptions=False):
# TODO done
# agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
agents = []
# agents = pacmanAgent[:layout.numPacman] + \
# ghostAgents[:layout.getNumGhosts()]
initState = GameState()
initState.initialize(layout)
for index, agentState in enumerate(initState.data.agentStates):
if agentState.isPacman:
agents.append(pacmanType(agentOpts, index, agentState))
else:
agents.append(ghostType(agentOpts, index, agentState))
game = Game(agents, display, self, catchExceptions=catchExceptions)
game.state = initState
initState.game = game
self.initialState = initState.deepCopy()
self.quiet = quiet
return game
def execute_one_game(self, weights):
"""
Execute one game / eval of a run.
Initialize the Pac controller with the given weights.
Return score.
"""
# Pick a new map and set up a new game state.
game_map = self.experiment.pre_loaded_maps[random.randint(0, 99)]
self.experiment.world_data = []
game_state = GameState(game_map, self.experiment.pill_density,
self.experiment.time_multiplier,
self.experiment.fruit_spawning_probability,
self.experiment.fruit_score,
self.experiment.num_pacs,
self.experiment.num_ghosts)
game_state.write_world_config(self.experiment.world_data)
game_state.write_world_time_score(self.experiment.world_data)
# Create a new Pac controller with a hard-coded expression tree
# to add a weighted sum of G, P, W, and F.
g_node = Node(expr='*',
left=Node('constant', constant=weights[0]),
right=Node('G'))
p_node = Node(expr='*',
left=Node('constant', constant=weights[1]),
right=Node('P'))
w_node = Node(expr='*',
left=Node('constant', constant=weights[2]),
right=Node('W'))
f_node = Node(expr='*',
left=Node('constant', constant=weights[3]),
right=Node('F'))
add1_node = Node(expr='+', left=g_node, right=p_node)
add2_node = Node(expr='+', left=w_node, right=f_node)
root_node = Node(expr='+', left=add1_node, right=add2_node)
self.pac_controllers[0] = PacController(0, ExprTree(root_node))
# While the game isn't over, play game turns.
game_over = False
while (not game_over):
game_over = game_state.play_turn(self.experiment.world_data,
self.pac_controllers,
self.ghost_controllers)
return game_state.score
def testExtration(self):
featureExt = FeatureExtractor()
agent = Player("aql agent")
enemy = Player("greedy agent")
gameState = GameState(agent, enemy)
enemy.accumulatedCards["Water"] += 1
enemy.accumulatedCards["Fire"] += 1
features = featureExt.getFeatures(gameState, "action", agent.name)
self.assertEqual(features["enemy-distance-to-closest-win"], 1)
self.assertEqual(features["agent-distance-to-closest-win"], 4)
agent.cards.append((1, "Water"))
enemy.accumulatedCards["Fire"] -= 1
enemy.accumulatedCards["Water"] += 1
features = featureExt.getFeatures(gameState, "action", agent.name)
self.assertEqual(features["agent-distance-to-closest-win"], 3)
self.assertEqual(features["enemy-distance-to-closest-win"], 1)
def __init__(self):
QMainWindow.__init__(self)
self._logger = StateLogger()
self._game_state = GameState(self._logger, self.hit_sound)
self._kom = Komunikator(self._game_state)
self._rx_thread = None
uic.loadUi('untitled.ui', self)
self.setWindowTitle("Statki")
self.mapaWroga = mapaGry(self._game_state,
False,
self.refresh_selection)
self.mapaNasza = mapaGry(self._game_state,
True,
self.refresh_selection)
self.mapEnemy.addWidget(self.mapaWroga)
self.mapOur.addWidget(self.mapaNasza)
# Associate buttons
self.b_connect.clicked.connect(self.client_init)
self.b_listen.clicked.connect(self.server_init)
self.b_lose.clicked.connect(self.instant_lose)
self.b_save_log.clicked.connect(self.log_popup)
self.b_load_log.clicked.connect(self.help_popup)
self.b_send.clicked.connect(self.send_message)
self.b_strzel.clicked.connect(self.shoot)
self.b_ustaw.clicked.connect(self.add_statek)
self.b_next.clicked.connect(self.get_next_state)
self.b_prev.clicked.connect(self.get_prev_state)
#sounds
self.s_splash = QtGui.QSound("res/splash.wav")
self.s_explode = QtGui.QSound("res/bombexpl.wav")
self.s_lose = QtGui.QSound("res/loser.wav")
self.startTimer(1000)
self.state_view_update()
def predict_next_move(self, game, current_player, search_iters):
''' Returns the (deterministic) best move choice predicted by the
agent. This method is for use when playing against
the agent (or two agents playing one another) in real-time. (E.g. via
the play() method of the UpDown class.)
Parameters
----------
game : UpDown
a game of upset-downset.
current_player : int or str
the current player to move. Can be either the integers 0/1
(see UP/DOWN in config.py) or the strings 'up'/'down'
(for Up/Down resp.).
search_iters : int (nonnegative)
the number of search iterations to perform during MCTS.
Returns
-------
int
the best action in 'game' for 'current_player' as predicted by the
agent via the deterministic MCTS policy (i.e., temp=0)
'''
# make sure the model is in evaluation mode.
self.model.eval()
# if passed a string for current player, convert
if isinstance(current_player, str):
player_dict = {'up': UP, 'down': DOWN}
current_player = player_dict[current_player.casefold()]
# get the MCTS policy
game_state = GameState(game, current_player)
root = PUCTNode(game_state)
policy = self.MCTS(root, search_iters, temp=0)
return np.argmax(policy)
def execute_one_game(self, pac_expr_tree):
"""
Execute one game / eval of a run given experiment data
and the root of an expression tree for controlling Pac.
Return score.
"""
# Pick a new map and set up a new game state.
game_map = self.experiment.pre_loaded_maps[random.randint(0, 99)]
self.experiment.world_data = []
game_state = GameState(game_map, self.experiment.pill_density,
self.experiment.time_multiplier,
self.experiment.fruit_spawning_probability,
self.experiment.fruit_score,
self.experiment.num_pacs,
self.experiment.num_ghosts)
game_state.write_world_config(self.experiment.world_data)
game_state.write_world_time_score(self.experiment.world_data)
# Create a new Pac controller
self.pac_controllers[0] = PacController(0, pac_expr_tree)
# While the game isn't over, play game turns.
game_over = False
while (not game_over):
game_over = game_state.play_turn(self.experiment.world_data,
self.pac_controllers,
self.ghost_controllers)
# Implement parsimony pressure
fitness = 0
if (self.parsimony_technique == 'size'):
fitness = game_state.score - (self.pppc * pac_expr_tree.root.size)
else:
fitness = game_state.score - (self.pppc *
pac_expr_tree.root.height)
return fitness, game_state.score
def __init__(self):
self.board = Board()
self.game_state = GameState()
self.selected_square = [0, 0]
self.possible_moves = []
self.white_turn = True
def run(self,
replay_buffer,
update_signal,
self_play_id,
search_iters=SELF_PLAY_SEARCH_ITERS,
markov_exp=SELF_PLAY_MARKOV_EXP,
temp=TEMP,
temp_thrshld=TEMP_THRSHLD):
'''Starts indefinite self-play loop. The games for self-play are
generated via an ongoing Markov chain as described in randomDag.py.
The self-play processes are synchronized with one another, train
and evaluation processes via the 'replay_buffer' and 'update_signal',
respectively. 'replay_buffer' stores the self-play data and triggers
the start of training while 'update_signal' triggers model parameter
updates.
Parameters
----------
replay_buffer : ReplayBuffer
remote actor for managing self-play data between self-play processes
and the Train process. Also carries the signal to start training.
update_signal : UpdateSignal
remote actor for synchronization between self-play processes and
evaluation processes. Triggers model parameter updates.
self_play_id : int (nonnegative)
unique identifier for the self-play process.
search_iters : int (positve), optional
the number of search iterations to perform during MCTS.
The default is SELF_PLAY_SEARCH_ITERS.
markov_exp : float, optional
The exponent determining the number of steps taken in
the markov chain in generating games for self-play.
temp : float (nonnegative)
partially controls exploration. If 0, the policy is deterministic
and the position with highest visit from MCTS is chosen.
temp_thrshld : int (nonnegative), optional
The number of moves after which the policy becomes determnistic.
I.e., temp is set to 0. (See temp, above.) The default is
TEMP_THRSHLD.
Returns
-------
None.
'''
# put agent in evaluation mode
self.agent.model.eval()
# the action space...
actions = np.arange(MAX_NODES)
# game state generator via an ongoing Markov chain
state_generator = GameState.state_generator(markov_exp)
# start indefinite self-play loop
while True:
# check for updates
if ray.get(update_signal.get_update.remote(self_play_id)):
# get current update_id
update_id = ray.get(update_signal.get_update_id.remote())
# load current alpha paramenters
self.agent.load_parameters(
path=f'./model_data/alpha_{update_id}.pt')
# reset the update signal
update_signal.clear_update.remote(self_play_id)
# get a game and play
initial_state = next(state_generator)
root = PUCTNode(initial_state)
states = []
policies = []
move_count = 0
while not root.state.is_terminal_state():
t = temp if move_count < temp_thrshld else 0
policy = self.agent.MCTS(root, search_iters, t)
move = np.random.choice(actions, p=policy)
states.append(root.state.encoded_state)
policies.append(policy)
root = root.edges[move]
root.to_root()
move_count += 1
# update state values as seen from current players perspective
if move_count % 2 == 0:
values = [(-1)**(i + 1) for i in range(move_count)]
else:
values = [(-1)**i for i in range(move_count)]
# construct training data from self-play
train_data = [
(state, policy, value)
for state, policy, value in zip(states, policies, values)
]
# add training data to replay buffer
replay_buffer.add.remote(train_data)
def menuLoop():
import ui
import otherEffects
done = False
hud = ui.HudScreen()
hud.set_button_text(0, "Play")
hud.set_button_text(1, "Editor")
hud.set_button_text(2, "Tutorial")
hud.set_button_text(3, "About")
hud.set_button_text(4, "Exit")
flag = None
pygame.mouse.set_visible(True)
pygame.event.set_grab(False)
fractal = otherEffects.ChaosObject(
(renderer.SCREEN_WIDTH // 2, (renderer.SCREEN_HEIGHT // 2) + 15), 225,
3)
s_field = otherEffects.StarField(renderer.SCREEN_SIZE)
hud.onChangedButton.append(s_field.change_speed)
while not done:
deltaTime = clock.get_time() / 1000
events = pygame.event.get()
flag = hud.update(deltaTime, events)
for event in events:
if event.type == pygame.QUIT:
return GameState.Quit
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_p:
return GameState.Quit
if hud.selected_button == GameState.Quit.value:
s_field.speed += deltaTime * 75
if s_field.speed > 100:
s_field.speed = 100
for x in range(10):
fractal.update()
s_field.update(deltaTime)
# region Buttons
s_field.draw()
renderer.SCREEN.blit(s_field, (0, 0))
fractal.draw(renderer.SCREEN)
textDraw.message_display_MT(renderer.SCREEN, "The dawn of Otrozhny",
renderer.SCREEN_WIDTH // 2, 100, 30)
textDraw.message_display_MT(renderer.SCREEN, "Containment breach",
renderer.SCREEN_WIDTH // 2, 150, 30)
if flag is not None:
return GameState(flag)
hud.draw()
pygame.display.update()
clock.tick()
return response['data']['games_won'], response['data']['board']
print("Error \'next\'\n" + str(response))
sys.exit()
def get_hash(hashString):
print("Saving hash to " + email + "_hash.txt")
f = open(email + "_hash.txt","w")
f.write("Email: " + email + "\nHash: " + hashString)
sys.exit()
##------------------------------------------------------------------------------------------
## Play the game
if __name__ == '__main__':
player = GameState()
state = new_game()
numMoves = 0
numGames = 0
startTime = time.time()
## Play games until hash recieved (challenge requires 50 games)
while True:
print('Game Number: {0}'.format(numGames))
print('Time Elapsed: {0:3f}'.format(time.time() - startTime))
## Make moves until game is over (games requires you make 30 moves without losing.)
while numMoves < 30:
## print(str(state[0]) + str(state[1]) + str(state[2]))
## print(str(state[3]) + str(state[4]) + str(state[5]))
## print(str(state[6]) + str(state[7]) + str(state[8]))
from gameState import GameState
if __name__ == '__main__':
print 'Starting Game of War'
game = GameState()
class GameInterface:
# Takes boardDim, a Point representing the board dimensions (8x8 board is represented by Point(8,8))
def __init__(self, boardDim):
self.gameOffset = Point()
self.boardOffset = Point()
self.gemImgs = {}
self.gameState = GameState(boardDim)
self.calibrate()
self.submitImg = bitmap.Bitmap.open(SUBMIT_IMAGE_PATH)
self.replayImg = bitmap.Bitmap.open(REPLAY_IMAGE_PATH)
# Sets the location of the top left of the board - all other points are represented relative to the gameOffset
def calibrate(self):
calibrationImg = bitmap.Bitmap.open(CALIBRATION_IMAGE_PATH)
bmp = bitmap.capture_screen()
(x, y) = bmp.find_bitmap(calibrationImg)
self.gameOffset = Point(x, y) + GAME_OFFSET
self.boardOffset = self.gameOffset + BOARD_OFFSET
# Reads the board from the screen and returns a GameState
def readGame(self):
bmp = bitmap.capture_screen()
submitPt = bmp.find_bitmap(self.submitImg)
replayPt = bmp.find_bitmap(self.replayImg)
if submitPt != None or replayPt != None:
if submitPt != None:
print 'submit found!'
mouse.move(submitPt[0], submitPt[1])
mouse.click()
time.sleep(10)
self.replayGame()
return self.gameState
for y in range(self.gameState.boardDim.y):
for x in range(self.gameState.boardDim.x):
gem = self.getGem(bmp, Point(x, y))
self.gameState.board.board[y][x] = gem
return self.gameState
# Returns a Gem given a screenshot and board coordinates
def getGem(self, bmp, point):
absPt = self.boardToAbsPt(point)
halfSize = 5
total = 0
totalColor = RGB()
for x in range(absPt.x - halfSize, absPt.x + halfSize):
for y in range(absPt.y - halfSize, absPt.y + halfSize):
hexColor = bmp.get_color(x, y)
r, g, b = color.hex_to_rgb(hexColor)
rgb = RGB(r, g, b)
totalColor += rgb
total += 1
avgRGB = totalColor / total
gemColor = self.RGBToGem(avgRGB)
return Gem(gemColor, 'status', point)
# Finds the gem color closest to the given RGB value
def RGBToGem(self, RGB):
minDistance = None;
color = 'none'
for key, value in COLOR_CONSTANTS.items():
if RGB.distSquared(value) < minDistance or minDistance == None:
color = key
minDistance = RGB.distSquared(value)
return color
# Click and drag the mouse to make a move - takes a Move object
# Attempts to place the cursor back where it found it
def makeMove(self, move):
self.gameState.makeMove(move)
firstPt, secondPt = move.pointTuple()
absFirst = self.boardToAbsPt(firstPt)
absSecond = self.boardToAbsPt(secondPt)
lastX, lastY = mouse.get_pos()
mouse.move(absFirst.x, absFirst.y)
mouse.toggle(True)
mouse.move(absSecond.x, absSecond.y)
mouse.toggle(False)
mouse.move(lastX, lastY)
# Move mouse off the board
def moveOffBoard(self):
mouse.move(self.gameOffset.x - 10, self.gameOffset.y - 10)
# Returns True if the mouse is in the exit box (10x10 pix block at the top left of the game)
def isMouseAtExit(self):
(x, y) = mouse.get_pos()
#return x > self.gameOffset.x and x < self.gameOffset.x + GAME_SIZE.x and y > self.gameOffset.y and y < self.gameOffset.y + GAME_SIZE.y
return x > self.gameOffset.x and x < self.gameOffset.x + 10 and y > self.gameOffset.y and y < self.gameOffset.y + 10
# Converts board coordinates to absolute screen coordinates (the center of the tile)
def boardToAbsPt(self, boardPt):
absX = self.boardOffset.x + boardPt.x * PIECE_OFFSET.x + PIECE_OFFSET.x / 2
absY = self.boardOffset.y + boardPt.y * PIECE_OFFSET.y + PIECE_OFFSET.y / 2
return Point(absX, absY)
def replayGame(self):
print 'replay!!!'
replayPt = None
time.sleep(5)
while replayPt == None:
bmp = bitmap.capture_screen()
replayPt = bmp.find_bitmap(self.replayImg)
mouse.move(replayPt[0], replayPt[1])
mouse.click()
time.sleep(60)
mouse.move(self.gameOffset.x + GAME_SIZE.x / 2, self.gameOffset.y + GAME_SIZE.y / 2)
mouse.click()
time.sleep(2)
mouse.move(self.gameOffset.x + 100, self.gameOffset.y + 100)
mouse.click()
def approximate_outcome(self, game, search_iters):
'''Approximates the outcome of game. For this to malke sense the game
must be a normal play short partisan combinatorial game (a combinatorial
game which ends in a finite number of moves and the last player to
move wins).
The approximate outcome is found by having the agent self-play
the game twice: once with each player starting. The results are
combined via the outcome rules for normal play short partisan
combintorial games to determine an approximate outcome.
(For more in for on combinatorila game see :
https://en.wikipedia.org/wiki/Combinatorial_game_theory)
Parameters
----------
game : UpDown
a game of upset-downset.
search_iters : int (positive),
the number of search iterations to perform during each MCTS.
Returns
-------
approx_out : str,
'Next', if the Next player (first player to move) wins.
'Previous', if the Previous player (second player to move) wins.
'Up', if Up can force a win. (Playing first or second).
'Down', if Down can force a win. (Playing first or second).
'''
# make sure the model is in evaluation mode.
self.model.eval()
# game state from each players persepective
up_start = GameState(game, UP)
down_start = GameState(game, DOWN)
# action space and stor for outcomes found.
actions = np.arange(MAX_NODES)
outcomes = []
#self-play, once with each player moving first
for game_state in [up_start, down_start]:
# set root
root = PUCTNode(game_state)
move_count = 0
# play until a terminal state is reached
while not root.state.is_terminal_state():
policy = self.MCTS(root, search_iters, temp=0)
move = np.random.choice(actions, p=policy)
root = root.edges[move]
root.to_root()
move_count += 1
# update outcome: 'P' for second player to move
# and 'N' for first player to move
out = 'P' if (move_count % 2 == 0) else 'N'
outcomes.append(out)
# get outcome prediction
up_start_out, down_start_out = outcomes
if up_start_out == 'P' and down_start_out == 'P':
approx_out = 'Previous'
elif up_start_out == 'N' and down_start_out == 'N':
approx_out = 'Next'
elif up_start_out == 'P' and down_start_out == 'N':
approx_out = 'Down'
elif up_start_out == 'N' and down_start_out == 'P':
approx_out = 'Up'
return approx_out
import pygame
from pygame import Color
from gameState import GameState
from bots.jack import Jack
from bots.sunny import Sunny
if __name__ == "__main__":
pygame.init()
clock = pygame.time.Clock()
gameWidth = 20
gameHeight = 15
blockWidth = 40
initPos = (gameWidth / blockWidth / 2, gameHeight / blockWidth / 2)
pygame.display.set_caption("Armageddon Blitz")
game = GameState(gameWidth, gameHeight, blockWidth)
game.add_bot(Jack(69), Color('orange'))
game.add_bot(Sunny(420), Color('red'))
game.run_game_loop()
from gameState import GameState
## Basic Fuzzing Test
## We have the GameState play against itself.
## Since it is a FSM designed to never reach a terminal state, the games should go on indefinitely.
## GameState() will ramdomly select a path given multiple possible moves allowing for complete path coverage eventually.
testPlayer = GameState()
## Run 100 games
for i in range(100):
state = list('---------')
## print("%c%c%c" %(state[0],state[1],state[2]))
## print("%c%c%c" %(state[3],state[4],state[5]))
## print("%c%c%c\n" %(state[6],state[7],state[8]))
## Play 100 moves
for _ in range(100):
update = testPlayer.get_next_move(state, player=1)
if len(update) == 1:
state[update[0] - 1] = 'p'
else:
state[update[0] - 1] = '-'
state[update[1] - 1] = 'p'
## print("Player 1")
## print("%c%c%c" %(state[0],state[1],state[2]))
## print("%c%c%c" %(state[3],state[4],state[5]))
## print("%c%c%c\n" %(state[6],state[7],state[8]))
class MyWin(QMainWindow):
def __init__(self):
QMainWindow.__init__(self)
self._logger = StateLogger()
self._game_state = GameState(self._logger, self.hit_sound)
self._kom = Komunikator(self._game_state)
self._rx_thread = None
uic.loadUi('untitled.ui', self)
self.setWindowTitle("Statki")
self.mapaWroga = mapaGry(self._game_state,
False,
self.refresh_selection)
self.mapaNasza = mapaGry(self._game_state,
True,
self.refresh_selection)
self.mapEnemy.addWidget(self.mapaWroga)
self.mapOur.addWidget(self.mapaNasza)
# Associate buttons
self.b_connect.clicked.connect(self.client_init)
self.b_listen.clicked.connect(self.server_init)
self.b_lose.clicked.connect(self.instant_lose)
self.b_save_log.clicked.connect(self.log_popup)
self.b_load_log.clicked.connect(self.help_popup)
self.b_send.clicked.connect(self.send_message)
self.b_strzel.clicked.connect(self.shoot)
self.b_ustaw.clicked.connect(self.add_statek)
self.b_next.clicked.connect(self.get_next_state)
self.b_prev.clicked.connect(self.get_prev_state)
#sounds
self.s_splash = QtGui.QSound("res/splash.wav")
self.s_explode = QtGui.QSound("res/bombexpl.wav")
self.s_lose = QtGui.QSound("res/loser.wav")
self.startTimer(1000)
self.state_view_update()
def get_prev_state(self):
self._game_state.set_logged_state(self._logger.get_prev_state())
def get_next_state(self):
self._game_state.set_logged_state(self._logger.get_next_state())
def timerEvent(self, *args, **kwargs):
if self._game_state.get_state_change():
self.state_view_update()
self.repaint()
def onParseEvent(self, event):
print event
def shoot(self):
sel_list = self.mapaWroga.get_selection()
if sel_list:
self.mapaWroga.clear_selection()
sel = sel_list[0]
self.repaint()
self._kom.send_shoot(sel)
else:
print "Brak zaznaczenia!"
def add_statek(self):
assert self._game_state.get_turn_no() == 0
try:
self.mapaNasza.add_new_ship()
except AssertionError:
print "Shipyard: Not enough parts for that kind of ship"
self.repaint()
def state_view_update(self):
if self._game_state.get_turn_no() == 0:
self.l_turn_indicator.setText("Rozmieszczanie statkow")
else:
if self._game_state.is_my_turn():
self.l_turn_indicator.setText("Nasza kolej")
else:
self.l_turn_indicator.setText("Ich kolej")
self.l_turn_no.setText(" ".join(["Numer tury:",
str(self._game_state.get_turn_no())]))
self.l_my_ile_1.setText(str(self._game_state.get_ship_count(1)))
self.l_my_ile_2.setText(str(self._game_state.get_ship_count(2)))
self.l_my_ile_3.setText(str(self._game_state.get_ship_count(3)))
self.l_my_ile_4.setText(str(self._game_state.get_ship_count(4)))
self.l_oni_ile_1.setText(str(self._game_state.get_enemy_ship_count(1)))
self.l_oni_ile_2.setText(str(self._game_state.get_enemy_ship_count(2)))
self.l_oni_ile_3.setText(str(self._game_state.get_enemy_ship_count(3)))
self.l_oni_ile_4.setText(str(self._game_state.get_enemy_ship_count(4)))
def refresh_selection(self, selection):
try:
x, y = selection;
self.l_wsp_x.setText(str.format("X:{}", x))
self.l_wsp_y.setText(str.format("Y:{}", y))
self.repaint()
except TypeError:
print "No new selections allowed."
def send_message(self):
try:
self._kom.send(self._komunikat)
except AttributeError:
print("Sender: not initialized")
def log_popup(self):
logFile = QtGui.QFileDialog.getOpenFileName()
self._logger.save_logger(logFile)
def help_popup(self):
popup = HelpDialog()
popup.exec_()
def socket_cleanup(self):
try:
self._kom.send("q")
self._kom.sender_terminate()
except AttributeError:
print("Sender: not initialized")
def closeEvent(self, event):
self._rx_thread = Thread()
self._kom.cleanup()
event.accept()
def client_init(self):
self.setWindowTitle("Statki - Klient")
if 1: #self._game_state.ready_for_battle():
# text, ok = QtGui.QInputDialog.getText(self, 'Wybor celu',
# 'Podaj wody na ktore '
# 'chcesz wyplynac (ip):')
if 1: #ok:
text = "localhost"
self._rx_thread = Thread(target=self._kom.client_init, args=(text,))
self._rx_thread.start()
else:
print "Generals: We're not ready to fight yet, commander!"
def server_init(self):
self.setWindowTitle("Statki - Serwer")
if 1: #self._game_state.ready_for_battle():
self._rx_thread = Thread(target=self._kom.server_init, args=())
self._rx_thread.start()
else:
print "Generals: We're not ready to fight yet, commander!"
def instant_lose(self):
self.s_lose.play()
sleep(6)
# winsound.PlaySound("res/loser.wav",(winsound.SND_ALIAS))
self.socket_cleanup()
qApp.exit(0)
def hit_sound(self,hit):
if hit:
self.s_explode.play()
else:
self.s_splash.play()
class Game():
"""
Game class instantiates dealer and player and the initial game state. It plays the game by playing
a sequence of hands until the player bustso or until the nHands value is reached (nHands should be used
when not using a user-agent so if the agent keeps winning the game doesnt go on forever)
"""
def __init__(self, verbose, agentType, nHands, startingMoney, nTraining):
"""
Initialize the game! Create dealer and player objects and an initial gameState
input: verbose
whether or not to print each step as agents play
input: agentType
string representing the type of agent to instantiate
input: nHands
number of hands to play at max if agent never busts
input: startingMoney
the amount of money the agent gets to start with
input: nTraining
the number of training hands to do for a qlearning player
returns: nothing
"""
self.verbose = verbose
self.agentType = agentType
self.nHands = int(nHands) + int(nTraining)
self.nStartingHands = int(nHands) + int(nTraining)
print("{} test {} train {} total".format(nHands, nTraining,
self.nHands))
self.startingMoney = startingMoney
self.nTraining = int(nTraining)
self.dealer = Dealer()
self.player = self.createAgent(self.agentType, self.startingMoney,
nTraining)
self.agents = [self.player, self.dealer]
# Clean slate
dealerHand = Hand()
playerHand = Hand()
deck = Deck()
# list because player can split
playerHands = [playerHand]
if self.player:
initialBets = [self.player.getBetAmt()]
# Create initial game state
self.gameState = GameState(verbose, self.dealer, dealerHand,
self.player, playerHands, deck,
initialBets)
self.q = self.agentType == 'qlearning'
def isValidGame(self):
""" Make sure we created the player correctly """
if self.player is None:
return False
return True
def createAgent(self, agentType, startingMoney, nTraining):
""" Create an agent of the right type
input: string agentType
type of agent to create
input: int startingMoney
how much money the agent starts off with
returns: An instantiated agent with startingMoney, or None if agent not supported yet
"""
if (agentType == 'user'):
return UserPlayer(startingMoney)
elif (agentType == 'optimal'):
return OptimalPlayer(startingMoney)
elif (agentType == 'expectimax'):
return Expectimax(startingMoney)
elif (agentType == 'q-learning' or agentType == 'qlearning'):
return QLearning(startingMoney, nTraining)
elif (agentType == 'random'):
return Random(startingMoney)
else:
print("Can't create other agent types at this point\n")
return None
def reportPerformance(self, aggregateOutcomes, payout, totalBet, moneyLeft,
maxAmtHad, minAmtHad):
"""
Take the values from the playGame loop and output a summary of player performance over the hands
Return the stats to the game so they can be passed to statEngine
"""
nHandsPlayed = sum(aggregateOutcomes.values())
aggregatePercentages = {
k: float(v) / float(nHandsPlayed)
for k, v in aggregateOutcomes.items()
}
totalWinnings = payout
houseEdge = -totalWinnings / float(totalBet)
print(
"Counting all splits as two hands, there were {} hands played by the agent who started with ${}\n"
.format(nHandsPlayed, self.startingMoney))
print("Most money ever had: {}\t Least money ever had: {}\n".format(
maxAmtHad, minAmtHad))
print("Money remaining after all hands: ${}\n".format(moneyLeft))
print(
"Total winnings {} on total bets of {} for a house edge of {:.1%}".
format(totalWinnings, totalBet, houseEdge))
for state, number in aggregateOutcomes.items():
print("{} : {} ({:.1%})\n".format(state, number,
aggregatePercentages[state]))
stats = {
'nHands': nHandsPlayed,
'outcomes': aggregateOutcomes,
'percentages': aggregatePercentages,
'totalWinnings': payout,
'totalBet': totalBet,
'houseEdge': houseEdge,
}
return stats
def playGame(self):
""" Loop through playing hands until agent out of money or we reach self.nHands
input: none
returns: stat dictionary with summary of performance
"""
if (self.verbose):
print(
"**** Welcome to CS182 Blackjack! ****\n\n\nNew game:\nYour starting money: {}\n"
.format(self.startingMoney))
# Performance bookkeeping
aggregateOutcomes = {
WinStates.WIN: 0,
WinStates.PUSH: 0,
WinStates.BLACKJACK: 0,
WinStates.LOSE: 0,
}
aggregatePayout = 0
aggregateBet = 0
minVal = int(self.startingMoney)
maxVal = int(self.startingMoney)
stats = None
# Game loop
while (True):
if self.nHands % 10000 == 0:
print(self.nHands)
# Play hand
winStateList, payout, betAmount = self.playHand()
# Performance tracking. Only track performance for Q-learner if it's out of training
bookkeep = True
if self.q:
if ((self.nStartingHands - self.nHands) < (self.nTraining)):
bookkeep = False
if bookkeep:
# Bookkeeping on performance
for winState in winStateList:
aggregateOutcomes[winState] += 1
aggregatePayout += payout
aggregateBet += betAmount
curMoney = self.gameState.player.getMoney()
if curMoney > maxVal:
maxVal = curMoney
if curMoney < minVal:
minVal = curMoney
# Reset hands
self.gameState.resetHands()
# if user player, ask if wants to play more
if self.agentType == 'user':
sleep(2)
cont = input("Another hand? y/n ---> ")
if cont == "n" or cont == "N" or cont == "no":
break
# Out of money or game over
if self.nHands == 0 or self.gameState.player.getMoney() <= 0:
stats = self.reportPerformance(aggregateOutcomes,
aggregatePayout, aggregateBet,
curMoney, maxVal, minVal)
# If qlearner, write the policy to disk
if self.q:
diskIO = QDictIO(self.player.QValues)
diskIO.write()
break
return stats
def playHand(self):
"""
Play a hand! deal to the player and dealer, get players actions, change gameState, get dealer's actions,
determine winner, etc
input: none
returns: Return winsState list for all hands, the payout across all hands, and the amount bet
(multiple hands mentioned in case fo split)
"""
vPrint("\n\n*************** NEW HAND ***************\n\n",
self.verbose)
self.nHands -= 1
# Place bet and deal
self.gameState.initialDeal()
vPrint(
"New hand: Player bet: {}\tPlayer money: {}\n".format(
self.player.getBetAmt(), self.player.getMoney()), self.verbose)
vPrint("...Dealing...\n", self.verbose)
# for storing last actions of each hand for qlearning updates
lastActions = []
lastNewStates = []
lastPrevStates = []
# Hand loop
while not self.gameState.isTerminal():
# Player turn
if self.gameState.isPlayerTurn():
vPrint("***** Player's turn *****\n\n", self.verbose)
for idx, hand in enumerate(self.gameState.getPlayerHands()):
vPrint(
"Player hand {}: {}\n".format(idx, hand.strFromHand()),
self.verbose)
vPrint(
"Player is currently playing hand {}\n".format(
self.gameState.getPlayerHandIdx()), self.verbose)
vPrint(
"Dealer's shown card: {}\n".format(
self.gameState.dealerHand.strFromHand()), self.verbose)
# Get action player takes in this state (will make sure its action for the hand they're playing)
playerAction = self.player.getAction(self.gameState)
vPrint("Player action is {}\n".format(playerAction),
self.verbose)
# Take the action
newGameState = self.gameState.generatePlayerSuccessor(
playerAction)
# If Q learner player, update them or store their last action to update after the dealer plays
if self.q:
# Still player turn, give a zero reward if playing same hand (didnt bust)
if newGameState.isPlayerTurn():
if newGameState.getPlayerHandIdx(
) == self.gameState.getPlayerHandIdx():
reward = 0
newGameState.player.update(self.gameState,
playerAction,
newGameState, reward)
else:
# Playing another hand, store the last action of this hand to update with hand rewards after eval
lastActions.append(playerAction)
lastPrevStates.append(self.gameState)
lastNewStates.append(newGameState)
# Its dealer turn, append the last action to update after hand eval
else:
lastActions.append(playerAction)
lastPrevStates.append(self.gameState)
lastNewStates.append(newGameState)
# Update the gamestate
self.gameState = newGameState
# Dealer turn
else:
vPrint("***** Dealer's turn ****** \n\n", self.verbose)
for idx, hand in enumerate(self.gameState.getPlayerHands()):
vPrint(
"Player hand {}: {}\n".format(idx, hand.strFromHand()),
self.verbose)
vPrint(
"Dealer's shown card: {}\n".format(
self.gameState.dealerHand.strFromHand()), self.verbose)
# Get dealers action
dealerAction = self.dealer.getAction(self.gameState)
vPrint("Dealer action is {}\n".format(dealerAction),
self.verbose)
# Take the action
self.gameState = self.gameState.generateDealerSuccessor(
dealerAction)
# Evaluate who won
winStates = self.gameState.getWinState()
totalBet = sum(self.gameState.getBets())
payouts = [
self.gameState.getPayout(winState, handIdx)
for handIdx, winState in enumerate(winStates)
]
# Update the qlearner with payouts based on their last actions
if self.q:
# Blackjack dealt so no actions, no update
if len(lastActions) != len(payouts):
pass
else:
# send an update for each tuple of (s,a,r,s')
for i in range(len(payouts)):
reward = payouts[i]
action = lastActions[i]
orig_state = lastPrevStates[i]
new_state = lastNewStates[i]
self.gameState.player.update(orig_state, action, new_state,
reward)
# Reset the lists of update items
lastActions = []
lastPrevStates = []
lastNewStates = []
# Get the total payout and apply it, return the results to the game loop
payout = reduce(lambda p1, p2: p1 + p2, payouts)
# vPrint the results of each hand and total payout
for idx, hand in enumerate(self.gameState.getPlayerHands()):
vPrint(
"=============\n\nHand {}:\nPlayer has {}, dealer has {}\n\nResult of hand is a {} for the player, payout is {}\n\n=============\n\n"
.format(idx, hand.getHandValue(),
self.gameState.dealerHand.getHandValue(),
winStates[idx], payouts[idx]), self.verbose)
vPrint("Total payout across all hands is {}\n".format(payout),
self.verbose)
self.gameState = self.gameState.applyPayout(payout)
return (winStates, payout, totalBet)
from boss import Boss
from battle import Battle
from politechnikomon import Politechnikomon
def set_done():
done = False
#Initialization
pygame.init()
screen = pygame.display.set_mode((800, 600))
#Data setup
map = Map('map.png', 'tiles.png')
gameState = GameState()
gui = GUI(gameState, screen)
player = Player('player.png')
boss = Boss('boss.png')
ppaix = Politechnikomon('pppix.png', 'Ppaix', 20)
paichu = Politechnikomon('paichu.png', 'Paichu', 20)
oirposan = Politechnikomon('oirposan.png', 'Oirposan', 20)
niespanier = Politechnikomon('niespanier.png', 'Niespanier', 50)
map.generate_map()
TILE_SIZE = 40
position = [0, 0]
#Main loop
while not gameState.done:
if (gameState.mode == 'map'):
screen.fill((0, 0, 0))
