def _run_ai_simulation(game_surface: pygame.Surface, size: int,
player1: Union[MobilityTreePlayer, PositionalTreePlayer, RandomPlayer,
ReversiGame, MCTSTimeSavingPlayer],
player2: Union[MobilityTreePlayer, PositionalTreePlayer, RandomPlayer,
ReversiGame, MCTSTimeSavingPlayer]) -> None:
if size == 8:
background = pygame.image.load('assets/gameboard8.png')
elif size == 6:
background = pygame.image.load('assets/gameboard6.png')
else:
raise ValueError("invalid size.")
game_surface.blit(background, (0, 0))
pygame.display.flip()
game = ReversiGame(size)
previous_move = '*'
board = game.get_game_board()
_draw_game_state(game_surface, background, size, board)
pass_move = pygame.image.load('assets/pass.png')
player1_side = BLACK
while game.get_winner() is None:
if previous_move == '*' or game.get_current_player() == player1_side:
move = player1.make_move(game, previous_move)
else:
move = player2.make_move(game, previous_move)
previous_move = move
game.make_move(move)
if move == 'pass':
surface = game_surface
game_surface.blit(pass_move, (300, 300))
pygame.display.flip()
pygame.time.wait(500)
game_surface.blit(surface, (0, 0))
pygame.display.flip()
else:
board = game.get_game_board()
_draw_game_state(game_surface, background, size, board)
pygame.time.wait(500)
winner = game.get_winner()
if winner == BLACK:
victory = pygame.image.load('assets/player1_victory.png')
game_surface.blit(victory, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
elif winner == WHITE:
defeat = pygame.image.load('assets/player2_victory.png')
game_surface.blit(defeat, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
else:
draw = pygame.image.load('assets/draw.png')
game_surface.blit(draw, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
def _minimax(self, root_move: tuple[int, int], depth: int,
game: ReversiGame, alpha: float, beta: float) -> GameTree:
"""
_minimax is a minimax function with alpha-beta pruning implemented that returns
a full GameTree where each node stores the given evaluation
Preconditions
- depth >= 0
"""
white_move = (game.get_current_player() == -1)
ret = GameTree(move=root_move, is_white_move=white_move)
# early return at max depth
if depth == self.depth:
ret.evaluation = heuristic(game, self.heuristic_list)
return ret
possible_moves = list(game.get_valid_moves())
if not possible_moves:
if game.get_winner() == 'white':
ret.evaluation = 10000
elif game.get_winner() == 'black':
ret.evaluation = -10000
else:
ret.evaluation = 0
return ret
random.shuffle(possible_moves)
best_value = float('-inf')
if not white_move:
best_value = float('inf')
for move in possible_moves:
new_game = game.copy_and_make_move(move)
new_tree = self._minimax(move, depth + 1, new_game, alpha, beta)
ret.add_subtree(new_tree)
# we update the alpha value when the maximizer is playing (white)
if white_move and best_value < new_tree.evaluation:
best_value = new_tree.evaluation
alpha = max(alpha, best_value)
if beta <= alpha:
break
# we update the beta value when the minimizer is playing (black)
elif not white_move and best_value > new_tree.evaluation:
best_value = new_tree.evaluation
beta = min(beta, best_value)
if beta <= alpha:
break
ret.evaluation = best_value
return ret
def _minimax(self, root_move: tuple[int, int], game: ReversiGame,
depth: int) -> GameTree:
"""
_minimax is a function that returns a tree where each node has a value determined by
the minimax search algorithm
"""
white_move = (game.get_current_player() == -1)
ret = GameTree(move=root_move, is_white_move=white_move)
# early return if we have reached max depth
if depth == self.depth:
ret.evaluation = heuristic(game, self.heuristic_list)
return ret
possible_moves = list(game.get_valid_moves())
# game is over if there are no possible moves in a position
if not possible_moves:
# if there are no moves, then the game is over so we check for the winner
if game.get_winner() == 'white':
ret.evaluation = 10000
elif game.get_winner() == 'black':
ret.evaluation = -10000
else:
ret.evaluation = 0
return ret
# shuffle for randomness
random.shuffle(possible_moves)
# best_value tracks the best possible move that the player can make
# this value is maximized by white and minimized by black
best_value = float('-inf')
if not white_move:
best_value = float('inf')
for move in possible_moves:
new_game = game.copy_and_make_move(move)
new_subtree = self._minimax(move, new_game, depth + 1)
if white_move:
best_value = max(best_value, new_subtree.evaluation)
else:
best_value = min(best_value, new_subtree.evaluation)
ret.add_subtree(new_subtree)
# update the evaluation value of the tree once all subtrees are added
ret.evaluation = best_value
return ret
def make_move(self, game: ReversiGame,
previous_move: Optional[str]) -> str:
"""Make a move given the current game.
previous_move is the opponent player's most recent move, or None if no moves
have been made.
Preconditions:
- There is at least one valid move for the given game state
- len(game.get_valid_moves) > 0
:param game: the current game state
:param previous_move: the opponent player's most recent move, or None if no moves
have been made
:return: a move to be made
"""
if self._tree is None: # initialize a tree if there is no tree
if previous_move is None:
self._tree = MCTSTree(START_MOVE, copy.deepcopy(game))
else:
self._tree = MCTSTree(previous_move, copy.deepcopy(game))
else: # update tree with previous move if there is a tree
if len(self._tree.get_subtrees()) == 0:
self._tree.expand()
if previous_move is not None:
self._tree = self._tree.find_subtree_by_move(previous_move)
assert self._tree.get_game_after_move().get_game_board(
) == game.get_game_board()
assert self._tree.get_game_after_move().get_current_player(
) == game.get_current_player()
for _ in range(self._n):
self._tree.mcts_round(self._c)
# update tree with the decided move
move = self._tree.get_most_confident_move()
self._tree = self._tree.find_subtree_by_move(move)
return move
def make_move(self, game: ReversiGame,
previous_move: Optional[str]) -> str:
"""Make a move given the current game.
previous_move is the opponent player's most recent move, or None if no moves
have been made.
Preconditions:
- There is at least one valid move for the given game state
- len(game.get_valid_moves) > 0
:param game: the current game state
:param previous_move: the opponent player's most recent move, or None if no moves
have been made
:return: a move to be made
"""
piece = game.get_current_player()
tree = self.build_minimax_tree(game,
piece,
depth=self._depth,
find_max=True,
previous_move=previous_move)
return tree.get_best()
# Window loop
while window.is_running():
""" UPDATE STUFF """
# Get game winner
winner = game.get_winner()
if winner is not None:
results.append(winner)
ui_handler.update_games_stored_text(len(results), window)
increment_player_score(winner, window)
game.start_game(human_player=game.get_human_player())
# If the game is not paused, look for mouse clicks and process moves.
if not ui_handler.get_game_paused():
if game.get_human_player() == game.get_current_player():
# Look at the mouse clicks and see if they are in the board.
for event in window.get_events():
if event[0] == pygame.MOUSEBUTTONUP:
square = board_manager.check_mouse_press(
event[1], game.get_board())
if square != (-1, -1):
if game.try_make_move(square):
moves_made.append(square)
elif game.get_winner() is None:
next_move = colour_to_player[
game.get_current_player()].make_move(game, moves_made[-1])
game.try_make_move(next_move)
moves_made.append(next_move)
def _run_ai_game(game_surface: pygame.Surface, size: int,
ai_player: Union[MobilityTreePlayer, PositionalTreePlayer, RandomPlayer,
ReversiGame, MCTSTimeSavingPlayer],
user_side: str = BLACK) -> None:
if size == 8:
background = pygame.image.load('assets/gameboard8.png')
elif size == 6:
background = pygame.image.load('assets/gameboard6.png')
else:
raise ValueError("invalid size.")
game_surface.blit(background, (0, 0))
pygame.display.flip()
game = ReversiGame(size)
previous_move = '*'
if user_side == BLACK:
ai_side: str = WHITE
else:
ai_side: str = BLACK
board = game.get_game_board()
_draw_game_state(game_surface, background, size, board)
pass_move = pygame.image.load('assets/pass.png')
while game.get_winner() is None:
if (previous_move == '*' and user_side == WHITE) or game.get_current_player() == user_side:
if game.get_valid_moves() == ['pass']:
game.make_move('pass')
previous_move = 'pass'
surface = game_surface
game_surface.blit(pass_move, (300, 300))
pygame.display.flip()
pygame.time.wait(1000)
game_surface.blit(surface, (0, 0))
pygame.display.flip()
continue
while True:
event = pygame.event.wait()
if event.type == pygame.MOUSEBUTTONDOWN:
mouse_pos = pygame.mouse.get_pos()
if 585 <= mouse_pos[0] <= 795 and 10 <= mouse_pos[1] <= 41:
return
else:
move = _search_for_move(mouse_pos, size)
print(move)
if move == '' or move not in game.get_valid_moves():
continue
else:
previous_move = move
game.make_move(move)
board = game.get_game_board()
_draw_game_state(game_surface, background, size, board)
pygame.time.wait(1000)
break
if event.type == pygame.QUIT:
return
else:
move = ai_player.make_move(game, previous_move)
previous_move = move
game.make_move(move)
if move == 'pass':
surface = game_surface
game_surface.blit(pass_move, (300, 300))
pygame.display.flip()
pygame.time.wait(1000)
game_surface.blit(surface, (0, 0))
pygame.display.flip()
else:
board = game.get_game_board()
_draw_game_state(game_surface, background, size, board)
winner = game.get_winner()
if winner == user_side:
victory = pygame.image.load('assets/victory.png')
game_surface.blit(victory, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
elif winner == ai_side:
defeat = pygame.image.load('assets/defeat.png')
game_surface.blit(defeat, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
else:
draw = pygame.image.load('assets/draw.png')
game_surface.blit(draw, (300, 300))
pygame.display.flip()
pygame.time.wait(3000)
return
