def play(self):
"""Function to play a game vs the AI."""
print("Start Human vs AI\n")
mcts = MonteCarloTreeSearch(self.net)
game = self.game.clone() # Create a fresh clone for each game.
game_over = False
value = 0
node = TreeNode()
print("Enter your move in the form: row, column. Eg: 1,1")
go_first = input("Do you want to go first: y/n?")
if go_first.lower().strip() == 'y':
print("You play as X")
human_value = 1
game.print_board()
else:
print("You play as O")
human_value = -1
# Keep playing until the game is in a terminal state.
while not game_over:
# MCTS simulations to get the best child node.
# If player_to_eval is 1 play as the Human.
# Else play as the AI.
if game.current_player == human_value:
action = input("Enter your move: ")
if isinstance(action, str):
action = [int(n, 10) for n in action.split(",")]
action = (1, action[0], action[1])
best_child = TreeNode()
best_child.action = action
else:
best_child = mcts.search(game, node,
CFG.temp_final)
action = best_child.action
game.play_action(action) # Play the child node's action.
game.print_board()
game_over, value = game.check_game_over(game.current_player)
best_child.parent = None
node = best_child # Make the child node the root node.
if value == human_value * game.current_player:
print("You won!")
elif value == -human_value * game.current_player:
print("You lost.")
else:
print("Draw Match")
print("\n")
def go(self):
print("One rule:\r\n Move piece form 'x,y' \r\n eg 1,3\r\n")
print("-" * 60)
print("Ready Go")
mc = MonteCarloTreeSearch(self.net, 1000)
node = TreeNode()
board = Board()
while True:
if board.c_player == BLACK:
action = input(f"Your piece is 'O' and move: ")
action = [int(n, 10) for n in action.split(",")]
action = action[0] * board.size + action[1]
next_node = TreeNode(action=action)
else:
_, next_node = mc.search(board, node)
board.move(next_node.action)
board.show()
next_node.parent = None
node = next_node
if board.is_draw():
print("-" * 28 + "Draw" + "-" * 28)
return
if board.is_game_over():
if board.c_player == BLACK:
print("-" * 28 + "Win" + "-" * 28)
else:
print("-" * 28 + "Loss" + "-" * 28)
return
board.trigger()
def play(self):
mcts = MonteCarloTreeSearch(self.net)
game = deepcopy(self.game)
game_over = False
value = 0
node = TreeNode()
valid = 0
# self.game.colorBoard()
game.print_board()
while not game_over:
if game.current_player == self.human_player:
valid = False
while valid == False:
piece, refpt, rot, flip = self.get_input(game)
piece.create(0, (refpt[0], refpt[1]))
f = 'None'
if flip == 0:
f == 'None'
else:
f = 'h'
piece.flip(f)
piece.rotate(90 * rot)
valid = game.valid_move(piece.points, self.human_player)
if valid == False:
print('You selected an illegal move, please reselect')
# print('attempting', piece.points)
# print('corners are ', game.corners[self.human_player])
if piece.ID not in ['I5', 'I4', 'I3', 'I2']:
encoding = (refpt[0] * 14 +
refpt[1]) * 91 + piece.shift + (
rot // 90) * 2 + flip
else:
encoding = (refpt[0] * 14 +
refpt[1]) * 91 + piece.shift + (
rot // 90) * 1 + flip
best_child = TreeNode()
best_child.action = encoding
print('CHOICE WAS MADE BY A HUMAN TO PLAY', piece.ID, '@',
refpt)
else:
best_child = mcts.search(game, node, CFG.temp_final)
action = best_child.action
game.play_action(action)
game.print_board()
# game.colorBoard()
game_over, value = game.check_game_over(game.current_player)
best_child.parent = None
node = best_child
if value == self.human_player * game.current_player:
print("You won!")
elif value == -self.human_player * game.current_player:
print("You lost.")
else:
print("Draw Match")
