def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
self.adult_last_move = "first"
self.change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_ord_matrix()
self.save_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.q_matrix = []
self.update_q_matrix()
# setup and initialize probability matrix
self.probability_matrix = []
self.update_probability_matrix()
def _mock_input(self, *args, **kwargs):
prompt = args[0]
if prompt.startswith("Wanna play?"):
# self.old_print(prompt, 'y')
return "y"
elif prompt.startswith("Enter dice to keep (no spaces), or (q)uit:"):
scorers = GameLogic.get_scorers(self.roll)
keepers = "".join([str(ch) for ch in scorers])
# self.old_print(prompt, keepers)
return keepers
elif prompt.startswith("(r)oll again, (b)ank your points or (q)uit "):
# _________________________________________________________
# our average score 9175
x = GameLogic.get_scorers(self.roll)
y = GameLogic.calculate_score(x)
# print(type(x))
# print("x"*50)
# print(x)
# print("x"*50)
if y < 200:
# scorers = GameLogic.get_scorers(self.roll)
# if keepers < 200:
return ("r")
else:
return "b"
# _________________________________________________________
# return "b"
else:
raise ValueError(f"Unrecognized prompt {prompt}")
def __init__(self):
self.game_map = GameMap()
self.game_logic = GameLogic(self.game_map)
self.view = View()
self.level_start()
self.enemy_actions()
self.player_actions()
self.check_game_status()
def _mock_input(self, *args, **kwargs):
prompt = args[0]
if prompt.startswith("Wanna play?"):
# self.old_print(prompt, 'y')
return "y"
elif prompt.startswith("Enter dice to keep (no spaces), or (q)uit:"):
scorers = GameLogic.get_scorers(self.roll)
keepers = "".join([str(ch) for ch in scorers])
if 5 in self.roll or 1 in self.roll:
"""
The kept dices will only works if the self.roll has 5(s) or 1(s)
"""
keepers = "".join([str(ch) for ch in scorers])
# self.old_print(prompt, keepers)
# self.old_print(keepers)
return keepers
elif prompt.startswith("(r)oll again, (b)ank your points or (q)uit "):
# scorers = GameLogic.get_scorers(self.roll)
# keepers = "".join([str(ch) for ch in scorers])
self.old_print(prompt, 'b')
if 5 in self.roll:
return ("b")
else:
return "r"
else:
raise ValueError(f"Unrecognized prompt {prompt}")
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
self.adult_last_move = "first"
self.change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_ord_matrix()
self.save_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.q_matrix = []
self.update_q_matrix()
# setup and initialize probability matrix
self.probability_matrix = []
self.update_probability_matrix()
class Main:
def __init__(self):
self._rand_sel = ComputerSelection()
self._game_logic = GameLogic()
self.mainFunction()
def mainFunction(self):
""" Initiates the program asking the user to select an option.
Calls for the random_selection function on computer_selection.
Compares both choices and gets a winner.
"""
print("")
print("WELCOME TO ROCK-PAPER-SCISSORS")
print("------------------------------")
question = [
inquirer.List(
"action",
"Make your choice",
["rock", "paper", "scissors", "lizard", "spock"],
)
]
user_choice = inquirer.prompt(question)["action"]
computer_choice = self._rand_sel.random_selection()
print("YOU: " + user_choice + " vs COMPUTER: " + computer_choice)
rps_winner = self._game_logic.winner(user_choice=user_choice,
machine_choice=computer_choice)
print(rps_winner)
class Main:
def __init__(self):
self.instances = []
# create instances
self.game_logic = GameLogic()
self.instances.append(self.game_logic)
self.instances.append(PlayerView(self.game_logic))
def go(self):
self.game_logic.load_world("level_1.txt")
while True:
for instance in self.instances:
instance.tick()
if self.game_logic.get_property("quit"):
break
def test_getplayer(self, mock_input):
captured_output = StringIO()
sys.stdout = captured_output
game = GameLogic()
self.assertIsInstance(game.player_1, Player)
self.assertIsInstance(game.player_2, Player)
self.assertEqual(game.player_1.name, 'ScaryTerry')
self.assertEqual(game.player_2.name, 'Holt')
sys.stdout = sys.__stdout__
def setUp(self):
pygame.font.init()
self.display = pygame.display.set_mode((1024, 640))
self.gamelogic = GameLogic()
self.colors = Colors()
self.startview = StartView(self.display, self.colors)
self.save = SaveGame(self.gamelogic)
self.menu = Menu(self.display, self.colors)
self.shop = Shop(self.display, self.colors, self.gamelogic)
pygame.init()
def _mock_input(self, *args):
prompt = args[0]
if prompt.startswith("Wanna play?"):
return "y"
elif prompt.startswith("Enter dice to keep (no spaces), or (q)uit:"):
scorers = GameLogic.get_scorers(self.roll)
keepers = "".join([str(ch) for ch in scorers])
return keepers
elif prompt.startswith("(r)oll again, (b)ank your points or (q)uit "):
return "b"
else:
raise ValueError(f"Unrecognized prompt {prompt}")
def start_new_game(self):
next_lvl = self.game_state.advance_level()
# if we finished all the levels
restart_game = False
if not next_lvl:
self.game_state.reset()
next_lvl = self.game_state.advance_level()
restart_game = True
# create new game
game_logic = GameLogic()
game_logic.game_state = self.game_state
game_logic.event_manager = self.event_manager
self.add_graphic_state(game_logic)
game_logic.build_level(next_lvl)
self.game_logic = game_logic
# Display message at the begining of each level
msg = 'Level %d' % self.game_state.current_level
self.add_graphic_state(TemporaryMessage(msg))
if restart_game:
self.add_graphic_state(StartMenu())
msg = 'Credit goes here'
self.add_graphic_state(TemporaryMessage(msg))
else:
self.add_graphic_state(EndLevel(True))
def gather_keepers(self, roll, keeper_string):
keepers = [int(ch) for ch in keeper_string]
while not GameLogic.validate_keepers(roll, keepers):
print("Cheater!!! Or possibly made a typo...")
print(",".join([str(i) for i in roll]))
keeper_string = input("Enter dice to keep (no spaces), or (q)uit: ")
if keeper_string.startswith("q"):
self.quit_game()
keepers = [int(ch) for ch in keeper_string]
return keepers
def __init__(self, username):
"""Initiates the game and takes as a parameter the users name"""
pygame.init()
self._screen = pygame.display.set_mode((1380, 800))
self._screen.fill((54, 48, 33))
self._username = username
self._board_font = pygame.font.SysFont('Arial', 15, bold=pygame.font.Font.bold)
# What stage of the game we are
self._game_status = 'PRE-SETUP'
# Board setup files
self._running = True
# Creates map and board graphics
self._create_sea_map()
self._vertical_horizontal_lines()
self._game_logic = GameLogic(self._username)
self.mark_active_boats([1100, 30])
self.mark_active_boats([1100, 200], 'human')
self._game_logic.setup_game()
while self._running:
for event in pygame.event.get():
# Populate ai boat dictionary
self._event_handler(event)
pygame.display.update()
def test_playerturn(self, mock_input):
captured_output = StringIO()
sys.stdout = captured_output
game = GameLogic()
game.player_turn()
self.assertEqual(game.moves.state['mid'],{'l':0,'m':1, 'r':0})
game.player_turn()
self.assertEqual(game.moves.state['mid'],{'l':2,'m':1, 'r':0})
sys.stdout = sys.__stdout__
def make_game_easier(self, request):
"""Given a number, automatically match same count of pairs and return match histories"""
game = get_by_urlsafe(request.urlsafe_game_key, Game)
if not game:
raise endpoints.NotFoundException('Game not found!')
if game.game_over:
raise endpoints.ForbiddenException('Illegal action: Game is already over.')
hint_num = request.hint_num
if hint_num <= 0:
raise endpoints.ForbiddenException('Illegal action: Can not receive a negative number.')
if hint_num * 2 >= 52 - game.matched:
raise endpoints.ForbiddenException('Illegal action: Can not use hint to win, try a smaller number.')
hint_histories = GameLogic.make_game_easier(game=game, hint_num=hint_num)
return HistoryForms(items=[h.to_form() for h in hint_histories])
def main():
"""Main function of the game. Creates all the classes that GameLoop-class needs to run
the game, and then starts the game by calling gameloop.start().
"""
display = pygame.display.set_mode((1024, 640))
pygame.display.set_caption("Clicker")
pygame.font.init()
colors = Colors()
event_queue = EventQueue()
startview = StartView(display, colors)
menu = Menu(display, colors)
logic = GameLogic()
shop = Shop(display, colors, logic)
save = SaveGame(logic)
game_loop = GameLoop(logic, display, colors, startview, event_queue, menu,
shop, save)
pygame.init()
game_loop.start()
def play_game(player_one, player_two):
player_mapping = {player_one: "A", player_two: "B"}
players_turn = player_one
next_player = player_two
playing = True
game_logic = GameLogic(player_mapping)
while playing:
game_logic.show_board()
choosen_pit = input(f"{players_turn}, choose a pit: ")
if choosen_pit and game_logic.valid_pit(players_turn, choosen_pit):
go_again = game_logic.move(players_turn, choosen_pit)
if game_logic.game_ended(player_one, player_two):
playing = False
elif not go_again:
players_turn, next_player = next_player, players_turn
else:
print("Invalid choice, try again.")
def make_match(self, request):
"""Makes a match. Returns matched cards' state with message"""
game = get_by_urlsafe(request.urlsafe_game_key, Game)
if not game:
raise endpoints.NotFoundException('Game not found!')
if game.game_over:
raise endpoints.ForbiddenException('Illegal action: Game is already over.')
pair_1 = request.guess_pair_1
pair_2 = request.guess_pair_2
if pair_1 == pair_2:
raise endpoints.ForbiddenException('Illegal action: Two guess index must be different!')
if pair_1 < 0 or pair_2 < 0 or pair_1 > 51 or pair_2 > 51:
raise endpoints.ForbiddenException('Illegal action: Guess num must between 0 and 52!')
try:
return GameLogic.match_pair(game=game, pair_1=pair_1, pair_2=pair_2)
except RuntimeError:
raise endpoints.ForbiddenException('Illegal action: Could not rematch a matched card')
def test_checkwinner(self, mock_input):
captured_output = StringIO()
sys.stdout = captured_output
game = GameLogic()
self.assertEqual(game.check_winner(), None)
game.moves.state['mid']['l'] = 1
game.moves.state['mid']['m'] = 1
game.moves.state['mid']['r'] = 1
self.assertEqual(game.check_winner(), 1)
game.moves.state['mid']['m'] = 2
self.assertEqual(game.check_winner(), None)
game.moves.state['top']['r'] = 2
game.moves.state['bot']['l'] = 2
self.assertEqual(game.check_winner(), 2)
game.moves.state['top']['l'] = 1
game.moves.state['bot']['l'] = 1
self.assertEqual(game.check_winner(), 1)
sys.stdout = sys.__stdout__
def take_action(self, roll, dice_to_keep):
remaining_dice = get_remaining_dice(roll, dice_to_keep)
dice_to_keep = str_to_tuple(dice_to_keep)
to_bank = self.banker.shelf(GameLogic.calculate_score(dice_to_keep))
print(
f"You have {to_bank} unbanked points and {remaining_dice} dice remaining"
)
roll_bank_quit = input("(r)oll again, (b)ank your points or (q)uit ")
if (roll_bank_quit == 'b'):
self.banking(to_bank)
self.round += 1
if self.round > self.num_rounds:
print(
f'Thanks for playing. You earned {self.banker.balance} points'
)
sys.exit()
self.new_round()
elif (roll_bank_quit == 'r'):
if remaining_dice == 0:
remaining_dice = 6
self.repeat_round(remaining_dice)
elif (roll_bank_quit == 'q'):
self.quit_game()
def __init__(self, manager, white_player='human', black_player='human'):
# game state members
self.game_manager = manager
self.game_running = False
self.needs_redraw = True
self.screen = None
self.clock = pygame.time.Clock()
# game components
self.game_logic = GameLogic(self)
self.game_grid = GameGrid(self)
self.game_board = None
self.white_player = None
self.black_player = None
self.initialize_players(white_player, black_player)
self.turn_manager = TurnManager(self)
self.highlighter = Highlighter(self)
self.logger = Logger(self)
self.buttons = {}
def is_zilch(self, roll):
if GameLogic.calculate_score(roll) == 0:
return True
else:
return False
class IntelligentAgent2(store.object):
# initialize all the member variables
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
# Set up for Parent
self.p_change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.p_ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_p_ord_matrix()
self.save_p_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.p_q_matrix = []
self.update_p_q_matrix()
# setup and initialize probability matrix
self.p_probability_matrix = []
self.update_p_probability_matrix()
# Set up for Child
self.c_change = 0
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# setup and initialize ordinal matrix
self.c_ord_matrix = []
self.ord_o = 0
self.ord_p = 0
self.ord_q = 0
self.ord_y = 0
self.update_c_ord_matrix()
self.save_c_ord_matrix()
# setup and initialize Q-value matrix
self.q_o = self.ai_logic.t[0]
self.q_p = self.ai_logic.u[0]
self.q_q = self.ai_logic.v[0]
self.q_y = self.ai_logic.w[0]
self.c_q_matrix = []
self.update_c_q_matrix()
# setup and initialize probability matrix
self.c_probability_matrix = []
self.update_c_probability_matrix()
def reset_p_learning_rate(self):
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
def reset_c_learning_rate(self):
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# function to calculate new ordinal matrix with new state and store the ordinal matrix
def update_ord_matrix(self):
self.update_p_ord_matrix()
self.update_c_ord_matrix()
def update_p_ord_matrix(self):
self.ai_logic.update_matrix()
self.p_ord_matrix = []
temp_matrix = [self.ai_logic.t[1], self.ai_logic.u[1], self.ai_logic.v[1], self.ai_logic.w[1]]
# for each action, rank the reward in terms of value. Giving a matrix representing 41 states
for i in range(4):
count = 1
for j in range(4):
if i == j:
continue
if temp_matrix[i] > temp_matrix[j]:
count += 1
self.p_ord_matrix.append(count)
def update_c_ord_matrix(self):
self.ai_logic.update_matrix()
self.c_ord_matrix = []
temp_matrix = [self.ai_logic.t[0], self.ai_logic.u[0], self.ai_logic.v[0], self.ai_logic.w[0]]
for i in range(4):
count = 1
for j in range(4):
if i == j:
continue
if temp_matrix[i] > temp_matrix[j]:
count += 1
self.c_ord_matrix.append(count)
# store ordinal values in the class to remember
def save_p_ord_matrix(self):
self.ord_t = self.p_ord_matrix[0]
self.ord_u = self.p_ord_matrix[1]
self.ord_v = self.p_ord_matrix[2]
self.ord_w = self.p_ord_matrix[3]
def save_c_ord_matrix(self):
self.ord_o = self.c_ord_matrix[0]
self.ord_p = self.c_ord_matrix[1]
self.ord_q = self.c_ord_matrix[2]
self.ord_y = self.c_ord_matrix[3]
# checks if the new calculated ordinal matrix is different to the old one, representing a change in state
def ord_matrix_change(self):
parent = self.p_ord_matrix_change()
child = self.c_ord_matrix_change()
if child == True or parent == True:
return True
else:
return False
def p_ord_matrix_change(self):
if (self.ord_t != self.p_ord_matrix[0] or self.ord_u != self.p_ord_matrix[1] or self.ord_v != self.p_ord_matrix[2] or self.ord_w != self.p_ord_matrix[3]):
self.save_p_ord_matrix()
self.p_change += 1
self.reset_p_learning_rate()
return True
return False
def c_ord_matrix_change(self):
if (self.ord_o != self.c_ord_matrix[0] or self.ord_p != self.c_ord_matrix[1] or self.ord_q != self.c_ord_matrix[2] or self.ord_y != self.c_ord_matrix[3]):
self.save_c_ord_matrix()
self.c_change += 1
self.reset_c_learning_rate()
return True
return False
# updates the q matrix with new values of Q for each state change
def update_q_matrix(self):
self.update_p_q_matrix()
self.update_c_q_matrix()
def update_p_q_matrix(self):
self.p_q_matrix = []
self.p_q_matrix.append(self.q_t)
self.p_q_matrix.append(self.q_u)
self.p_q_matrix.append(self.q_v)
self.p_q_matrix.append(self.q_w)
def update_c_q_matrix(self):
self.c_q_matrix = []
self.c_q_matrix.append(self.q_o)
self.c_q_matrix.append(self.q_p)
self.c_q_matrix.append(self.q_q)
self.c_q_matrix.append(self.q_y)
# updates the Q value for action t
def update_q_t(self):
if self.t_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.t_count
self.q_t = self.q_t + learning_rate*(self.ai_logic.t[1] + (self.discount * max(self.p_q_matrix)) - self.q_t)
# updates the Q value for action u
def update_q_u(self):
if self.u_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.u_count
self.q_u = self.q_u + learning_rate*(self.ai_logic.u[1] + (self.discount * max(self.p_q_matrix)) - self.q_u)
# updates the Q value for action v
def update_q_v(self):
if self.v_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.v_count
self.q_v = self.q_v + learning_rate*(self.ai_logic.v[1] + (self.discount * max(self.p_q_matrix)) - self.q_v)
# updates the Q value for action w
def update_q_w(self):
if self.w_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.w_count
self.q_w = self.q_w + learning_rate*(self.ai_logic.w[1] + (self.discount * max(self.p_q_matrix)) - self.q_w)
# updates the Q value for action o
def update_q_o(self):
if self.o_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.o_count
self.q_o = self.q_o + learning_rate*(self.ai_logic.t[0] + (self.discount * max(self.c_q_matrix)) - self.q_o)
# updates the Q value for action p
def update_q_p(self):
if self.p_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.p_count
self.q_p = self.q_p + learning_rate*(self.ai_logic.u[0] + (self.discount * max(self.c_q_matrix)) - self.q_p)
# updates the Q value for action q
def update_q_q(self):
if self.q_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.q_count
self.q_q = self.q_q + learning_rate*(self.ai_logic.v[0] + (self.discount * max(self.c_q_matrix)) - self.q_q)
# updates the Q value for action y
def update_q_y(self):
if self.y_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.y_count
self.q_y = self.q_y + learning_rate*(self.ai_logic.w[0] + (self.discount * max(self.c_q_matrix)) - self.q_y)
# updates the probability matrix after a change in Q values
def update_probability_matrix(self):
self.update_p_probability_matrix()
self.update_c_probability_matrix()
def update_p_probability_matrix(self):
self.p_probability_matrix = []
self.update_p_q_matrix()
total = 0
for i in range(4):
total += self.k ** self.p_q_matrix[i]
self.p_probability_matrix.append((self.k**self.q_t)/total)
self.p_probability_matrix.append((self.k**self.q_u)/total)
self.p_probability_matrix.append((self.k**self.q_v)/total)
self.p_probability_matrix.append((self.k**self.q_w)/total)
def update_c_probability_matrix(self):
self.c_probability_matrix = []
self.update_c_q_matrix()
total = 0
for i in range(4):
total += self.k ** self.c_q_matrix[i]
self.c_probability_matrix.append((self.k**self.q_o)/total)
self.c_probability_matrix.append((self.k**self.q_p)/total)
self.c_probability_matrix.append((self.k**self.q_q)/total)
self.c_probability_matrix.append((self.k**self.q_y)/total)
# checks if the AI won the game given an action pair
def check_win(self, adult, child):
return self.ai_logic.check_win(adult, child)
# returns the number of rounds the AI has played
def get_round(self):
return self.ai_logic.round
def parent_move(self):
# random number from 0 to 1
choice = random.random()
# determine the probablistic range of each action
prob_t = self.p_probability_matrix[0]
prob_u = self.p_probability_matrix[0]+self.p_probability_matrix[1]
prob_v = self.p_probability_matrix[0]+self.p_probability_matrix[1]+self.p_probability_matrix[2]
prob_w = 1
# if t is chosen, adult attends
if choice < prob_t:
adult = "attend"
# if u is chosen, adult ignores
if prob_t <= choice and choice < prob_u:
adult = "ignore"
# if v is chosen, adult attends
if prob_u <= choice and choice < prob_v:
adult = "attend"
# if w is chosen, adult ignores
if prob_v <= choice and choice < prob_w:
adult = "ignore"
return adult
def child_move(self):
# random number from 0 to 1
choice = random.random()
# determine the probablistic range of each action
prob_o = self.c_probability_matrix[0]
prob_p = self.c_probability_matrix[0]+self.c_probability_matrix[1]
prob_q = self.c_probability_matrix[0]+self.c_probability_matrix[1]+self.c_probability_matrix[2]
prob_y = 1
# if t is chosen, adult attends
if choice < prob_o:
child = "go"
# if u is chosen, adult ignores
if prob_o <= choice and choice < prob_p:
child = "go"
# if v is chosen, adult attends
if prob_p <= choice and choice < prob_q:
child = "dontgo"
# if w is chosen, adult ignores
if prob_q <= choice and choice < prob_y:
child = "dontgo"
return child
def move(self):
adult = self.parent_move()
child = self.child_move()
# find which action was chosen and update the specific Q value and probability matrix
if adult == "attend" and child == "go":
self.ai_logic.attend_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
# checks if the action caused a change in the ordinal matrix
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.t_count += 1
self.update_q_t()
if not self.check_final_action("child"):
self.o_count += 1
self.update_q_o()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "attend" and child == "dontgo":
self.ai_logic.attend_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.v_count += 1
self.update_q_v()
if not self.check_final_action("child"):
self.q_count += 1
self.update_q_q()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "go":
self.ai_logic.ignore_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.u_count += 1
self.update_q_u()
if not self.check_final_action("child"):
self.update_q_p()
self.p_count += 1
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "dontgo":
self.ai_logic.ignore_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.w_count += 1
self.update_q_w()
if not self.check_final_action("child"):
self.y_count += 1
self.update_q_y()
self.update_q_matrix()
self.update_probability_matrix()
return [adult, child]
def check_final_action(self, player):
if player == "adult":
for i in range(4):
if round(self.p_probability_matrix[i], 2) == 1.00:
return True
return False
elif player == "child":
for i in range(4):
if round(self.c_probability_matrix[i], 2) == 1.00:
return True
return False
def keep_scorers(self, roll):
return GameLogic.get_scorers(roll)
def calculate_score(self, roll):
return GameLogic.calculate_score(roll)
# game setup
# init pygame
pygame.init()
# init display
screen = pygame.display.set_mode(cfg['game']['screen_size'])
# level creation
levels = [LevelMic(screen, cfg['game']['screen_size'], mic)]
# choose level
level = levels[0]
# game logic with dependencies
game_logic = GameLogic()
# add clock
clock = pygame.time.Clock()
# mic stream and update
with mic.stream:
# game loop
while game_logic.run_loop:
for event in pygame.event.get():
# input handling
game_logic.event_update(event)
level.event_update(event)
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
# Set up for Parent
self.p_change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.p_ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_p_ord_matrix()
self.save_p_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.p_q_matrix = []
self.update_p_q_matrix()
# setup and initialize probability matrix
self.p_probability_matrix = []
self.update_p_probability_matrix()
# Set up for Child
self.c_change = 0
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# setup and initialize ordinal matrix
self.c_ord_matrix = []
self.ord_o = 0
self.ord_p = 0
self.ord_q = 0
self.ord_y = 0
self.update_c_ord_matrix()
self.save_c_ord_matrix()
# setup and initialize Q-value matrix
self.q_o = self.ai_logic.t[0]
self.q_p = self.ai_logic.u[0]
self.q_q = self.ai_logic.v[0]
self.q_y = self.ai_logic.w[0]
self.c_q_matrix = []
self.update_c_q_matrix()
# setup and initialize probability matrix
self.c_probability_matrix = []
self.update_c_probability_matrix()
class App(object):
def __init__(self):
self.game_map = GameMap()
self.game_logic = GameLogic(self.game_map)
self.view = View()
self.level_start()
self.enemy_actions()
self.player_actions()
self.check_game_status()
def start_render(self):
self.render_entities()
self.render_hud()
self.render = self.view.root.after(60, self.start_render)
def stop_render(self):
self.view.root.after_cancel(self.render)
def render_entities(self):
self.view.delete_entities()
self.view.draw_entities(self.game_logic.entities)
def render_hud(self):
self.view.delete_hud()
self.view.draw_HUD(self.game_logic.hero)
def enemy_actions(self):
for enemy in self.game_logic.entities[1:]:
self.game_logic.move_entity(enemy, enemy.get_move_direction())
self.game_logic.enemy_strike(enemy)
self.game_logic.remove_dead_entity(enemy)
self.view.root.after(500, self.enemy_actions)
def player_actions(self):
self.move_player()
self.player_strike()
self.view.root.after(60, self.player_actions)
def move_player(self):
if self.view.player_move_direction != None:
self.game_logic.move_entity(self.game_logic.hero,
self.view.player_move_direction)
self.view.player_move_direction = None
def player_strike(self):
if self.view.player_strike and self.game_logic.hero.can_strike:
self.game_logic.hero.strike()
self.view.player_strike = False
def check_game_status(self):
if self.game_logic.is_level_over():
self.stop_render()
self.level_start()
elif self.game_logic.hero.current_hp <= 0:
self.stop_render()
self.view.clear_view()
self.view.game_over_screen()
self.view.root.after(1500, exit)
self.view.root.after(2000, self.check_game_status)
def show_game_level(self):
self.view.clear_view()
self.view.write_level_info(self.game_logic.current_level + 1)
self.view.root.after(1000, self.view.clear_view)
def level_start(self):
self.show_game_level()
self.view.root.after(1100, self.game_logic.start_level)
self.view.root.after(
1200, lambda: self.view.draw_map(self.game_map.game_map))
self.view.root.after(1200, self.start_render)
class IntelligentAgent(store.object):
# initialize all the member variables
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
self.adult_last_move = "first"
self.change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_ord_matrix()
self.save_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.q_matrix = []
self.update_q_matrix()
# setup and initialize probability matrix
self.probability_matrix = []
self.update_probability_matrix()
def reset_learning_rate(self):
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# function to calculate new ordinal matrix with new state and store the ordinal matrix
def update_ord_matrix(self):
self.ai_logic.update_matrix()
self.ord_matrix = []
temp_matrix = [self.ai_logic.t[1], self.ai_logic.u[1], self.ai_logic.v[1], self.ai_logic.w[1]]
# for each action, rank the reward in terms of value. Giving a matrix representing 41 states
for i in range(4):
count = 1
for j in range(4):
if i == j:
continue
if temp_matrix[i] > temp_matrix[j]:
count += 1
self.ord_matrix.append(count)
# store ordinal values in the class to remember
def save_ord_matrix(self):
self.ord_t = self.ord_matrix[0]
self.ord_u = self.ord_matrix[1]
self.ord_v = self.ord_matrix[2]
self.ord_w = self.ord_matrix[3]
# checks if the new calculated ordinal matrix is different to the old one, representing a change in state
def ord_matrix_change(self):
if (self.ord_t != self.ord_matrix[0] or self.ord_u != self.ord_matrix[1] or self.ord_v != self.ord_matrix[2] or self.ord_w != self.ord_matrix[3]):
self.save_ord_matrix()
return True
return False
# updates the q matrix with new values of Q for each state change
def update_q_matrix(self):
self.q_matrix = []
self.q_matrix.append(self.q_t)
self.q_matrix.append(self.q_u)
self.q_matrix.append(self.q_v)
self.q_matrix.append(self.q_w)
# updates the Q value for action t.
def update_q_t(self):
if self.t_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.t_count
self.q_t = self.q_t + learning_rate*(self.ai_logic.t[1] + (self.discount * max(self.q_matrix)) - self.q_t)
# updates the Q value for action u
def update_q_u(self):
if self.u_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.u_count
self.q_u = self.q_u + learning_rate*(self.ai_logic.u[1] + (self.discount * max(self.q_matrix)) - self.q_u)
# updates the Q value for action v
def update_q_v(self):
if self.v_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.v_count
self.q_v = self.q_v + learning_rate*(self.ai_logic.v[1] + (self.discount * max(self.q_matrix)) - self.q_v)
# updates the Q value for action w
def update_q_w(self):
if self.w_count == 0:
learning_rate = 1
else:
learning_rate = 1.0/self.w_count
self.q_w = self.q_w + learning_rate*(self.ai_logic.w[1] + (self.discount * max(self.q_matrix)) - self.q_w)
# updates the probability matrix after a change in Q values
def update_probability_matrix(self):
self.probability_matrix = []
self.update_q_matrix()
total = 0
for i in range(4):
total += self.k ** self.q_matrix[i]
self.probability_matrix.append((self.k**self.q_t)/total)
self.probability_matrix.append((self.k**self.q_u)/total)
self.probability_matrix.append((self.k**self.q_v)/total)
self.probability_matrix.append((self.k**self.q_w)/total)
# function called to inform the AI to make a move and returns the action in the form of [adult, child]
def move(self):
# random number from 0 to 1
choice = random.random()
# determine the probablistic range of each action
prob_t = self.probability_matrix[0]
prob_u = self.probability_matrix[0]+self.probability_matrix[1]
prob_v = self.probability_matrix[0]+self.probability_matrix[1]+self.probability_matrix[2]
prob_w = 1
# if t is chosen, adult attends
if choice < prob_t:
adult = "attend"
# if u is chosen, adult ignores
if prob_t <= choice and choice < prob_u:
adult = "ignore"
# if v is chosen, adult attends
if prob_u <= choice and choice < prob_v:
adult = "attend"
# if w is chosen, adult ignores
if prob_v <= choice and choice < prob_w:
adult = "ignore"
# find the action choice of the inner child given an adult's action choice
child = self.child_move(adult)
# find which action was chosen and update the specific Q value and probability matrix
if adult == "attend" and child == "go":
self.ai_logic.attend_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
# checks if the action caused a change in the ordinal matrix
if self.ord_matrix_change():
self.change += 1
self.reset_learning_rate()
self.t_count += 1
self.update_q_t()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "attend" and child == "dontgo":
self.ai_logic.attend_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
if self.ord_matrix_change():
self.change += 1
self.reset_learning_rate()
self.v_count += 1
self.update_q_v()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "go":
self.ai_logic.ignore_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
if self.ord_matrix_change():
self.change += 1
self.reset_learning_rate()
self.u_count += 1
self.update_q_u()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "dontgo":
self.ai_logic.ignore_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
if self.ord_matrix_change():
self.change += 1
self.reset_learning_rate()
self.w_count += 1
self.update_q_w()
self.update_q_matrix()
self.update_probability_matrix()
return [adult, child]
# this function prompts the child to choose an action given the adult's choice.
# It picks the action with the greatest reward, if equal, then a random choice between go and don't go
def child_move(self, adult):
if self.adult_last_move == "first":
self.adult_last_move = adult
child_action = random.random()
if child_action <= 0.5:
return "go"
else:
return "dontgo"
if self.adult_last_move == "attend":
self.adult_last_move = adult
if self.ai_logic.t[0] > self.ai_logic.v[0]:
return "go"
elif self.ai_logic.t[0] < self.ai_logic.v[0]:
return "dontgo"
elif self.ai_logic.t[0] == self.ai_logic.v[0]:
child_action = random.random()
if child_action < 0.5:
return "go"
else:
return "dontgo"
if self.adult_last_move == "ignore":
self.adult_last_move = adult
if self.ai_logic.u[0] > self.ai_logic.w[0]:
return "go"
elif self.ai_logic.u[0] < self.ai_logic.w[0]:
return "dontgo"
elif self.ai_logic.u[0] == self.ai_logic.w[0]:
child_action = random.random()
if child_action < 0.5:
return "go"
else:
return "dontgo"
# checks if the AI won the game given an action pair
def check_win(self, adult, child):
return self.ai_logic.check_win(adult, child)
# returns the number of rounds the AI has played
def get_round(self):
return self.ai_logic.round
def __init__(self):
self._rand_sel = ComputerSelection()
self._game_logic = GameLogic()
self.mainFunction()
def setUp(self):
self.logic = GameLogic()
class TestGameLogic(unittest.TestCase):
def setUp(self):
self.logic = GameLogic()
def test_clicking_produces_score(self):
self.logic.click()
self.assertEqual(self.logic.score, 1)
def test_cannot_purchase_click_upgrade_without_score(self):
self.logic.score = 30
self.logic.click_upgrade()
self.assertEqual(self.logic.score, 30)
self.assertEqual(self.logic.click_upgrades_bought, 0)
def test_can_purchase_click_upgrade_with_sufficent_score(self):
self.logic.score = 50
self.logic.click_upgrade()
self.assertEqual(self.logic.score, 0)
self.assertEqual(self.logic.click_upgrades_bought, 1)
def test_click_upgrade_is_working(self):
self.logic.score = 50
self.logic.click_upgrade()
self.assertEqual(self.logic.score_per_click, 2)
self.logic.click()
self.assertEqual(self.logic.score, 2)
self.logic.score += 148
self.logic.click_upgrade()
self.assertEqual(self.logic.score_per_click, 4)
self.logic.click()
self.assertEqual(self.logic.score, 4)
def test_cannot_purchase_autoclicker_without_score(self):
self.logic.score = 50
self.logic.autoclicker()
self.assertEqual(self.logic.score, 50)
self.assertEqual(self.logic.autoclickers_bought, 0)
def test_can_purchase_autoclicker_with_sufficent_score(self):
self.logic.score = 100
self.logic.autoclicker()
self.assertEqual(self.logic.score, 0)
self.assertEqual(self.logic.autoclickers_bought, 1)
def test_autoclicker_upgrade_is_working(self):
self.logic.score = 400
self.logic.autoclickers_bought = 1
self.logic.autoclicker_upgrade()
self.assertEqual(self.logic.score, 0)
self.assertEqual(self.logic.autoclicker_upgrades_bought, 1)
self.assertEqual(self.logic.autoclicker_power, 2)
self.assertEqual(self.logic.autoclicker_upgrade_cost, 1200)
def test_cannot_buy_autoclicker_upgrade_without_autoclickers(self):
self.logic.score = 400
self.logic.autoclickers_bought = 0
self.logic.autoclicker_upgrade()
self.assertEqual(self.logic.score, 400)
self.assertEqual(self.logic.autoclicker_upgrades_bought, 0)
def test_autoclickers_are_working(self):
self.logic.autoclickers_bought = 2
self.logic.autoclicker_click()
self.assertEqual(self.logic.score, 2)
self.logic.score = 400
self.logic.autoclicker_upgrade()
self.logic.autoclicker_click()
self.assertEqual(self.logic.score, 4)
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
# Set up for Parent
self.p_change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.p_ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_p_ord_matrix()
self.save_p_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.p_q_matrix = []
self.update_p_q_matrix()
# setup and initialize probability matrix
self.p_probability_matrix = []
self.update_p_probability_matrix()
# Set up for Child
self.c_change = 0
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# setup and initialize ordinal matrix
self.c_ord_matrix = []
self.ord_o = 0
self.ord_p = 0
self.ord_q = 0
self.ord_y = 0
self.update_c_ord_matrix()
self.save_c_ord_matrix()
# setup and initialize Q-value matrix
self.q_o = self.ai_logic.t[0]
self.q_p = self.ai_logic.u[0]
self.q_q = self.ai_logic.v[0]
self.q_y = self.ai_logic.w[0]
self.c_q_matrix = []
self.update_c_q_matrix()
# setup and initialize probability matrix
self.c_probability_matrix = []
self.update_c_probability_matrix()
class IntelligentAgent2(store.object):
# initialize all the member variables
def __init__(self):
# AI contains the Game Logic
self.ai_logic = GameLogic()
# discount factor in the Q-learning algorithm
self.discount = 0.4
# k value for exploration
self.k = 2
# Set up for Parent
self.p_change = 0
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
# setup and initialize ordinal matrix
self.p_ord_matrix = []
self.ord_t = 0
self.ord_u = 0
self.ord_v = 0
self.ord_w = 0
self.update_p_ord_matrix()
self.save_p_ord_matrix()
# setup and initialize Q-value matrix
self.q_t = self.ai_logic.t[1]
self.q_u = self.ai_logic.u[1]
self.q_v = self.ai_logic.v[1]
self.q_w = self.ai_logic.w[1]
self.p_q_matrix = []
self.update_p_q_matrix()
# setup and initialize probability matrix
self.p_probability_matrix = []
self.update_p_probability_matrix()
# Set up for Child
self.c_change = 0
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# setup and initialize ordinal matrix
self.c_ord_matrix = []
self.ord_o = 0
self.ord_p = 0
self.ord_q = 0
self.ord_y = 0
self.update_c_ord_matrix()
self.save_c_ord_matrix()
# setup and initialize Q-value matrix
self.q_o = self.ai_logic.t[0]
self.q_p = self.ai_logic.u[0]
self.q_q = self.ai_logic.v[0]
self.q_y = self.ai_logic.w[0]
self.c_q_matrix = []
self.update_c_q_matrix()
# setup and initialize probability matrix
self.c_probability_matrix = []
self.update_c_probability_matrix()
def reset_p_learning_rate(self):
self.t_count = 0
self.u_count = 0
self.v_count = 0
self.w_count = 0
def reset_c_learning_rate(self):
self.o_count = 0
self.p_count = 0
self.q_count = 0
self.y_count = 0
# function to calculate new ordinal matrix with new state and store the ordinal matrix
def update_ord_matrix(self):
self.update_p_ord_matrix()
self.update_c_ord_matrix()
def update_p_ord_matrix(self):
self.ai_logic.update_matrix()
self.p_ord_matrix = []
temp_matrix = [
self.ai_logic.t[1], self.ai_logic.u[1], self.ai_logic.v[1],
self.ai_logic.w[1]
]
# for each action, rank the reward in terms of value. Giving a matrix representing 41 states
for i in range(4):
count = 1
for j in range(4):
if i == j:
continue
if temp_matrix[i] > temp_matrix[j]:
count += 1
self.p_ord_matrix.append(count)
def update_c_ord_matrix(self):
self.ai_logic.update_matrix()
self.c_ord_matrix = []
temp_matrix = [
self.ai_logic.t[0], self.ai_logic.u[0], self.ai_logic.v[0],
self.ai_logic.w[0]
]
for i in range(4):
count = 1
for j in range(4):
if i == j:
continue
if temp_matrix[i] > temp_matrix[j]:
count += 1
self.c_ord_matrix.append(count)
# store ordinal values in the class to remember
def save_p_ord_matrix(self):
self.ord_t = self.p_ord_matrix[0]
self.ord_u = self.p_ord_matrix[1]
self.ord_v = self.p_ord_matrix[2]
self.ord_w = self.p_ord_matrix[3]
def save_c_ord_matrix(self):
self.ord_o = self.c_ord_matrix[0]
self.ord_p = self.c_ord_matrix[1]
self.ord_q = self.c_ord_matrix[2]
self.ord_y = self.c_ord_matrix[3]
# checks if the new calculated ordinal matrix is different to the old one, representing a change in state
def ord_matrix_change(self):
parent = self.p_ord_matrix_change()
child = self.c_ord_matrix_change()
if child == True or parent == True:
return True
else:
return False
def p_ord_matrix_change(self):
if (self.ord_t != self.p_ord_matrix[0]
or self.ord_u != self.p_ord_matrix[1]
or self.ord_v != self.p_ord_matrix[2]
or self.ord_w != self.p_ord_matrix[3]):
self.save_p_ord_matrix()
self.p_change += 1
self.reset_p_learning_rate()
return True
return False
def c_ord_matrix_change(self):
if (self.ord_o != self.c_ord_matrix[0]
or self.ord_p != self.c_ord_matrix[1]
or self.ord_q != self.c_ord_matrix[2]
or self.ord_y != self.c_ord_matrix[3]):
self.save_c_ord_matrix()
self.c_change += 1
self.reset_c_learning_rate()
return True
return False
# updates the q matrix with new values of Q for each state change
def update_q_matrix(self):
self.update_p_q_matrix()
self.update_c_q_matrix()
def update_p_q_matrix(self):
self.p_q_matrix = []
self.p_q_matrix.append(self.q_t)
self.p_q_matrix.append(self.q_u)
self.p_q_matrix.append(self.q_v)
self.p_q_matrix.append(self.q_w)
def update_c_q_matrix(self):
self.c_q_matrix = []
self.c_q_matrix.append(self.q_o)
self.c_q_matrix.append(self.q_p)
self.c_q_matrix.append(self.q_q)
self.c_q_matrix.append(self.q_y)
# updates the Q value for action t
def update_q_t(self):
if self.t_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.t_count
self.q_t = self.q_t + learning_rate * (
self.ai_logic.t[1] +
(self.discount * max(self.p_q_matrix)) - self.q_t)
# updates the Q value for action u
def update_q_u(self):
if self.u_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.u_count
self.q_u = self.q_u + learning_rate * (
self.ai_logic.u[1] +
(self.discount * max(self.p_q_matrix)) - self.q_u)
# updates the Q value for action v
def update_q_v(self):
if self.v_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.v_count
self.q_v = self.q_v + learning_rate * (
self.ai_logic.v[1] +
(self.discount * max(self.p_q_matrix)) - self.q_v)
# updates the Q value for action w
def update_q_w(self):
if self.w_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.w_count
self.q_w = self.q_w + learning_rate * (
self.ai_logic.w[1] +
(self.discount * max(self.p_q_matrix)) - self.q_w)
# updates the Q value for action o
def update_q_o(self):
if self.o_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.o_count
self.q_o = self.q_o + learning_rate * (
self.ai_logic.t[0] +
(self.discount * max(self.c_q_matrix)) - self.q_o)
# updates the Q value for action p
def update_q_p(self):
if self.p_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.p_count
self.q_p = self.q_p + learning_rate * (
self.ai_logic.u[0] +
(self.discount * max(self.c_q_matrix)) - self.q_p)
# updates the Q value for action q
def update_q_q(self):
if self.q_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.q_count
self.q_q = self.q_q + learning_rate * (
self.ai_logic.v[0] +
(self.discount * max(self.c_q_matrix)) - self.q_q)
# updates the Q value for action y
def update_q_y(self):
if self.y_count == 0:
learning_rate = 1
else:
learning_rate = 1.0 / self.y_count
self.q_y = self.q_y + learning_rate * (
self.ai_logic.w[0] +
(self.discount * max(self.c_q_matrix)) - self.q_y)
# updates the probability matrix after a change in Q values
def update_probability_matrix(self):
self.update_p_probability_matrix()
self.update_c_probability_matrix()
def update_p_probability_matrix(self):
self.p_probability_matrix = []
self.update_p_q_matrix()
total = 0
for i in range(4):
total += self.k**self.p_q_matrix[i]
self.p_probability_matrix.append((self.k**self.q_t) / total)
self.p_probability_matrix.append((self.k**self.q_u) / total)
self.p_probability_matrix.append((self.k**self.q_v) / total)
self.p_probability_matrix.append((self.k**self.q_w) / total)
def update_c_probability_matrix(self):
self.c_probability_matrix = []
self.update_c_q_matrix()
total = 0
for i in range(4):
total += self.k**self.c_q_matrix[i]
self.c_probability_matrix.append((self.k**self.q_o) / total)
self.c_probability_matrix.append((self.k**self.q_p) / total)
self.c_probability_matrix.append((self.k**self.q_q) / total)
self.c_probability_matrix.append((self.k**self.q_y) / total)
# checks if the AI won the game given an action pair
def check_win(self, adult, child):
return self.ai_logic.check_win(adult, child)
# returns the number of rounds the AI has played
def get_round(self):
return self.ai_logic.round
def parent_move(self):
# random number from 0 to 1
choice = random.random()
# determine the probablistic range of each action
prob_t = self.p_probability_matrix[0]
prob_u = self.p_probability_matrix[0] + self.p_probability_matrix[1]
prob_v = self.p_probability_matrix[0] + self.p_probability_matrix[
1] + self.p_probability_matrix[2]
prob_w = 1
# if t is chosen, adult attends
if choice < prob_t:
adult = "attend"
# if u is chosen, adult ignores
if prob_t <= choice and choice < prob_u:
adult = "ignore"
# if v is chosen, adult attends
if prob_u <= choice and choice < prob_v:
adult = "attend"
# if w is chosen, adult ignores
if prob_v <= choice and choice < prob_w:
adult = "ignore"
return adult
def child_move(self):
# random number from 0 to 1
choice = random.random()
# determine the probablistic range of each action
prob_o = self.c_probability_matrix[0]
prob_p = self.c_probability_matrix[0] + self.c_probability_matrix[1]
prob_q = self.c_probability_matrix[0] + self.c_probability_matrix[
1] + self.c_probability_matrix[2]
prob_y = 1
# if t is chosen, adult attends
if choice < prob_o:
child = "go"
# if u is chosen, adult ignores
if prob_o <= choice and choice < prob_p:
child = "go"
# if v is chosen, adult attends
if prob_p <= choice and choice < prob_q:
child = "dontgo"
# if w is chosen, adult ignores
if prob_q <= choice and choice < prob_y:
child = "dontgo"
return child
def move(self):
adult = self.parent_move()
child = self.child_move()
# find which action was chosen and update the specific Q value and probability matrix
if adult == "attend" and child == "go":
self.ai_logic.attend_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
# checks if the action caused a change in the ordinal matrix
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.t_count += 1
self.update_q_t()
if not self.check_final_action("child"):
self.o_count += 1
self.update_q_o()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "attend" and child == "dontgo":
self.ai_logic.attend_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.v_count += 1
self.update_q_v()
if not self.check_final_action("child"):
self.q_count += 1
self.update_q_q()
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "go":
self.ai_logic.ignore_go()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.u_count += 1
self.update_q_u()
if not self.check_final_action("child"):
self.update_q_p()
self.p_count += 1
self.update_q_matrix()
self.update_probability_matrix()
if adult == "ignore" and child == "dontgo":
self.ai_logic.ignore_dontgo()
self.ai_logic.update_matrix()
self.update_ord_matrix()
self.ord_matrix_change()
if not self.check_final_action("adult"):
self.w_count += 1
self.update_q_w()
if not self.check_final_action("child"):
self.y_count += 1
self.update_q_y()
self.update_q_matrix()
self.update_probability_matrix()
return [adult, child]
def check_final_action(self, player):
if player == "adult":
for i in range(4):
if round(self.p_probability_matrix[i], 2) == 1.00:
return True
return False
elif player == "child":
for i in range(4):
if round(self.c_probability_matrix[i], 2) == 1.00:
return True
return False
