def fight(ann, best, dump=False):
board = game.getEmptyBoard()
current = 'O'
action = random.randint(0, game.BOARD_SIZE * game.BOARD_SIZE - 1)
board, won = game.move(board, action, current)
if dump:
print(game.render(board))
current = 'O' if current == 'X' else 'X'
while True:
entry = state_lists_to_batch([board], current)
probs = best(entry)[0] if current == 'O' else ann(entry)[0]
while True:
action = np.argmax(probs)
if action not in game.possible_moves(board):
probs[action] = -np.inf
else:
break
board, won = game.move(board, action, current)
if dump:
print(game.render(board))
if len(game.possible_moves(board)) == 0:
return None
if won:
break
current = 'O' if current == 'X' else 'X'
return best if current == 'O' else ann
def user_play(game):
key = KEY_RIGHT # init key
prevKey = key
while True:
key_arr = read_stdin()
if len(key_arr) == 0:
key = prevKey
elif len(key_arr) == 3:
if key_arr[2] == 'A':
key = KEY_UP
if key_arr[2] == 'B':
key = KEY_DOWN
if key_arr[2] == 'C':
key = KEY_RIGHT
if key_arr[2] == 'D':
key = KEY_LEFT
prevKey = key
game.move_snake(key)
game.update()
game.render()
print "render_dxy_state"
print "----------------"
game.render_dxy_state()
print "----------------"
if game.done:
break
time.sleep(0.25)
def main():
while True:
game.handle_inputs()
game.update()
game.render()
def displayInventory(self): li = self.niceInv() choice = reqs.selectionScreen(li, 'Inventory:', ORANGE, '[Enter] to select | [R] to dump | [Q] to exit', ORANGE, True) if choice[0] != None and choice[1] != None: if self.inv[choice[0]].type == 'food': self.eatObj(self.inv[choice[0]]) elif self.inv[choice[0]].type == 'weapon' and self.mode == 0: self.weaponChoice = choice[0] #self.atkObj(self.inv[choice[0]]) game.render()
def displayInventory(self):
li = self.niceInv()
choice = reqs.selectionScreen(
li, 'Inventory:', ORANGE,
'[Enter] to select | [R] to dump | [Q] to exit', ORANGE, True)
if choice[0] != None and choice[1] != None:
if self.inv[choice[0]].type == 'food':
self.eatObj(self.inv[choice[0]])
elif self.inv[choice[0]].type == 'weapon' and self.mode == 0:
self.weaponChoice = choice[0]
#self.atkObj(self.inv[choice[0]])
game.render()
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(size))
# model.add(Activation('linear'))
# print(model.summary())
# Finally, we configure and compile our agent. You can use every
# built-in Keras optimizer and even the metrics!
# model.compile(SGD(lr=1e-3), metrics=['mae'])
model.compile(optimizer='adam', loss='mae', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=100, batch_size=32,
validation_data=(x_val, y_val))
for i in range(10):
print("Before:")
game.render(x_val[i][:-1].reshape(shape))
print("After:")
game.render(y_val[i].reshape(shape))
print("Prediction:")
game.render(np.round(
model.predict(x_val[i].reshape(1, 17))).reshape(shape))
# Okay, now it's time to learn something! We visualize the training
# here for show, but this slows down training quite a lot. You can
# always safely abort the training prematurely using Ctrl + C.
# model.fit(env, nb_steps=50000, verbose=2)
# test:
# dqn.test(env, nb_episodes=5, visualize=True)
def render(self, mode='human', close=False):
# Render the environment to the screen
game.render(self.arr)
raw_input("Enter to continue.")
os.system('clear')
playerOne.load("./save/cfOne2000.h5")
playerTwo.load("./save/cfTwo2000.h5")
done = 0
batch_size = 42
for e in range(EPISODES):
cf.reset()
state = cf.board
state = np.reshape(state, [1, state_size])
time = 0 # time is used just to count frames as a measurement of how long the ai lasted
while True:
sleep(5)
time += 1
actionOne = playerOne.act(state)
moveOne = cf.dropTile(actionOne, 1)
cf.render()
print("/////////////////")
currentBoard = np.reshape(cf.board, [1, state_size])
actionTwo = playerTwo.act(currentBoard)
moveTwo = cf.dropTile(actionTwo, -1)
cf.render()
print("/////////////////")
nextStateOne = cf.board
backupBoard = cf.board
currentBoard = np.reshape(cf.board, [1, state_size])
cf.dropTile(playerOne.act(currentBoard), 1)
nextStateTwo = cf.board
cf.board = backupBoard
rewardOne = time
rewardTwo = time
stats = stats.Stats()
score_sum = 0.0
time_sum = 0.0
score_cnt = 0.0
steps_wo_r = 0
quality_max = 0.0
for e in range(EPISODES):
game.reset()
state = game.get_state()
for t in range(MAX_STEPS):
action = agent.act(state)
key = action2key[game.key][action]
if int(e / 100) * 100 == e:
game.render()
print "key:", key2str[key], " action:", action2str[
action], " time:", t
quality = score_sum / (score_cnt + 1)
msg_str = "episode: {}/{}, epsilon: {:.2}, q: {:0.2f}, mem: {}, mem_done: {}, time: {}"\
.format(e, EPISODES, agent.epsilon, quality, len(agent.memory), len(agent.memory_done), time_sum/100.0)
print msg_str
# print "----------------"
# game.render_dxy_state()
# print "----------------"
time.sleep(0.05)
next_state, reward = game.step(key)
#if reward == 0:
# steps_wo_r += 1
#else:
delta_time = 0
clock = pygame.time.Clock()
t = pygame.Surface((width,height))
t.set_alpha(180)
t.fill((0,0,0))
image = pygame.image.load("img/miljo.png")
image = pygame.transform.scale(image,(width, height))
while(1):
delta_time = float(clock.tick(400))
for event in pygame.event.get():
if(event.type == pygame.QUIT):
sys.exit()
game.input(delta_time)
game.update(delta_time)
screen.blit(image,(0,0,width,height))
game.render(screen)
screen.blit(t, (0,0))
pygame.display.flip()
assert_equal(len(map.keys()), 26)
assert_equal(isinstance(map, dict), True)
assert_equal(len(map['Path A'].keys()), 4)
assert_equal('about' in map['Path A'], True)
assert_equal('stuff' in map['Path A'], True)
assert_equal('enemy' in map['Path A'], True)
assert_equal('neighbors' in map['Path A'], True)
assert_equal(isinstance(map['Path A'], dict), True)
assert_equal(isinstance(map['Path A']['about'], str), True)
assert_equal(isinstance(map['Path A']['stuff'], list), True)
assert_equal(isinstance(map['Path A']['enemy'], bool), True)
assert_equal(isinstance(map['Path A']['neighbors'], list), True)
#render(x)
test_world = create_world()
assert_equal(render(test_world), '\nThere is a fork in the road\nTo the right is the path,\nto the left are the woods,\nand next to you is a path to a broken down house.\nYour health is 5/5\n********************************************************************************************************************\n\n')
assert_equal(render(test_world), render_location(test_world) + render_player(test_world))
#render_location(x)
test_world = create_world()
location = test_world['player']['location']
assert_equal(render_location(test_world), test_world['map'][location]['about'])
#render_enemy(x)
test_world = create_world()
test_world['player']['location'] = 'Bear'
assert_equal(render_enemy(test_world), bear(test_world))
test_world['player']['location'] = 'Tunnel'
assert_equal(render_enemy(test_world), tunnel(test_world))
test_world['player']['location'] = 'Path E'
assert_equal(render_enemy(test_world), path_e(test_world))
pygame.display.set_icon(screen_icon)
clock = pygame.time.Clock()
player1 = User(PieceColor.WHITE)
player2 = SmartBot(PieceColor.BLACK)
game = game.Game([player1, player2], BOX_WIDTH)
while True:
click = None
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
if event.type == pygame.MOUSEBUTTONDOWN:
row = math.floor(event.pos[1] / BOX_WIDTH)
column = math.floor(event.pos[0] / BOX_WIDTH)
click = board.get_box(row, column)
text_click = text_select.get_text_select()
if text_click:
click = text_click
text_select.set_text_select(None)
game.handle_turn(game.pieces, click)
game.render(display_surface, BOX_WIDTH, colors.BLACK, colors.WHITE, font)
pygame.display.update()
clock.tick(FPS)