def __init__(self, master=None, height=0, width=0):
tkinter.Canvas.__init__(self, master, height=height, width=width)
self.step_record_chess_board = Record.Step_Record_Chess_Board()
# 初始化記步器
self.init_chess_board_points() # 畫點
self.init_chess_board_canvas() # 畫棋盤
self.board = MCTS.Board()
self.AI = MCTS.MonteCarlo(self.board, 2)
self.clicked = 0
def __init__(self, master=None, height=0, width=0):
Tkinter.Canvas.__init__(self, master, height=height, width=width)
self.step_record_chess_board = Record.Step_Record_Chess_Board()
# 初始化記步器
self.height = 15
self.width = 15
self.init_chess_board_points() # 畫點
self.init_chess_board_canvas() # 畫棋盤
self.board = MCTS.Board()
self.n_in_row = 5
self.n_playout = 400 # num of simulations for each move
self.c_puct = 5
"""
Important 1: Python is pass by reference
So the self.board will be modified by other operations
"""
self.AI = MCTS.MonteCarlo(self.board, 1)
self.AI_1 = MCTS.MonteCarlo(self.board, 0)
self.clicked = 1
self.init = True # first place is given by user (later need to be replaced as a random selection)
self.train_or_play = True # True - train, False - play
self.step = 0
self.text_id = None
def generate_games(self,
episodes,
snapshots,
batch_size,
sim_time=0,
rollouts_per_move=0,
generate_random=False):
# geenrate a game and add to replay_buffer
print("Net will be cahced after the following episodes:", snapshots)
self.actor.save(self.name + "0")
if generate_random:
generator = actors.Random(self.state_manager)
else:
generator = self.actor
for i in range(1, episodes + 1):
board = self.state_manager.get_start()
MC = MCTS.MonteCarlo(self.start_player, self.state_manager,
generator)
while True:
# Do rollouts for sim_time seconds/ rollouts_per_move rollouts
MC.search(sim_time=sim_time, simulations=rollouts_per_move)
distribution = MC.get_move_distribution()
self.replay_buffer.append((board, distribution))
# Get next state based on rollouts
board, move = MC.best_move()
winner = self.state_manager.winner(board)
if self.verbose:
self.state_manager.print_move(move)
self.state_manager.print_board(board)
print()
# set new root
MC.purge_tree(board)
if winner != 0:
break
# lock before accessing shared actor
self.actor.train_network_random_minibatch(self.replay_buffer,
batch_size=batch_size)
if i in snapshots:
self.actor.save(self.name + str(i))
M = 10000 # Number of simulations per move in actual game
N = 25 # Number of starting stones
K = 15 # Max number of stones a player can pick
win_game_count_p1 = 0
game_count = 0
for g in range(G):
if P == 0:
start_player = random.randint(1, 2)
else:
start_player = P
s_m = state_manager_nim.state_manager_nim(N, K, start_player)
MC = MCTS.MonteCarlo(start_player, s_m)
while True:
MC.search(M)
best_state, move = MC.best_move()
if verbose:
# player that made move, is the player whose turn it's not
print("Player ", 1 if best_state[0] == 2 else 2, " made move: ", move,
" :: Current state: ", best_state[1], sep='')
winner = s_m.winner(best_state)
if winner != 0:
game_count += 1
print(game_count, "of", G, "games done")
if winner == 1:
win_game_count_p1 += 1
if verbose:
print("Player", winner, "wins!")
