def main():
# Create a 9x9 board
game = GobangGame(n=9, nir=9)
network = NNetWrapper(game)
if args.load_model:
network.load_checkpoint(args.checkpoint)
coach = Coach(game, network, args)
coach.learn()
#SET beta = 1 DURING TESTING SINCE x SHOULD BE UNKNOWN DURING TESTING.
'tempThreshold':
10, #dictates when the MCTS starts returning deterministic polices (vector of 0 and 1's). See Coach.py for more details.
'alpha':
1e-5, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller alpha is, the more weight the algorithm gives in selecting a sparse solution.
'gamma':
1, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller gamma is, the more likely algorithm is going to choose stopping action earlier(when ||x||_0 is small). gamma enforces how much we want to enforce Ax is close to y.
#choice of gamma is heavily dependent on the distribution of our signal and the distribution of entries of A. gamma should be apx. bigger than m/||A_Sx^* - y||_2^2, where y = Ax, and x^* is the solution to the l2 regression problem.
'epsilon':
1e-5, #If x is the optimal solution to l2, and the residual of l2 regression ||A_Sx-y||_2^2 is less than epsilon, then the state corresponding to indices S is a terminal state in MCTS.
}
#START ALPHAZERO TRAINING:
#Initialize Game_args, nnet, Game, and Alphazero
Game_rules = Game_args()
Game = CSGame()
nnet = NNetWrapper(args)
if args['load_nn_model'] == True:
filename = 'best'
nnet.load_checkpoint(args['network_checkpoint'], filename)
Alphazero_train = Coach(Game, nnet, args, Game_rules)
if args['load_training'] == True:
print('Load trainExamples from file')
Alphazero_train.loadTrainExamples()
#Start Training Alphazero
Alphazero_train.learn()
#Initialize Algorithms object to compare algorithms
Algorithms = CSAlgorithms()
#INITIALIZE ALPHAZERO FOR PREDICTION
#--------------------------------------------------------------
#initialize Game_args
#load sensing_matrix into game_args
game_args = Game_args()
matrix_filename = 'sensing_matrix.npy'
A = np.load(matrix_filename)
game_args.sensing_matrix = A
#initialize neural network wrapper object
#load weights and model we wish to predict with using nnet.load_checkpoint
nnet = NNetWrapper(args)
model_filename = 'best'
nnet.load_checkpoint(os.getcwd(), model_filename)
#initialize a new game object
new_game = CSGame()
#initialize skip_nnet if option is turned on
if args['skip_rule'] == 'bootstrap':
skip_nnet = NNetWrapper(args)
skip_nnet.load_checkpoint(args['skip_nnet_folder'],
args['skip_nnet_filename'])
elif args['skip_rule'] == None:
skip_nnet = nnet
else:
skip_nnet = None
#Initialize Alphazero
for x in range(7):
for y in range(7):
action2move.append((None, None, x, y))
for x in range(7):
for y in range(7):
for dx, dy in _directions_2:
if {x, y, x + dx, y + dy} <= set(range(7)):
action2move.append((x, y, x + dx, y + dy))
action2move.append((None, None, None, None))
g = AtaxxGame()
args1 = dotdict({'numMCTSSims': 2000, 'cpuct': 1.0})
n1 = NNet(g)
n1.load_checkpoint('.', 'best.pth.tar')
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
player = __file__[2]
board = []
actions = {
} # { key: piece(start position), value: list of position(destination position) }
# make board
input_lines = input_str.split("\n")
for i in range(7):
line = input_lines[i + 1].split(" ")
dic = {1: -1, 2: 1, 0: 0}
line = [dic.get(n, n) for n in line]
board.append(line)
# Suppress logging from fireplace
logger = logging.getLogger("fireplace")
logger.setLevel(logging.WARNING)
# logging.disable(logging.WARNING) # disable logging
# logging.disable(logging.NOTSET) # reenable logging
# all players
hp = HumanPlayer(g).play
rp = RandomPlayer(g).play
# nnet players
n1 = NNet()
#n1.nnet.cuda()
# n1.load_checkpoint('./temp/', '0.pth.tar')
# n1.load_checkpoint('./temp/', 'best18-287k-75i.pth.tar') # newest network
n1.load_checkpoint('../remote/models/', '0-32.pth.tar')
argsNN = dotdict(
{
'numMCTSSims': 25,
'cpuct': 1.0
}
) # minimum numMCTSSims = 2 to always find a valid action (at least end turn)
mcts1 = MCTS(g, n1, argsNN)
#a1p = lambda x: mcts1.getActionProb(x, temp=0)
a1p = functools.partial(
mcts1.getActionProb, temp=0
) # temp=1 means we pick an action by probability, temp=0 always takes the best action (most visited edge. random if more than 1 best action is available)
n2 = NNet()
# n2.load_checkpoint('./temp/', '0.pth.tar')
# n2.load_checkpoint('./temp/', 'temp18.pth.tar')
'x_l2' : True, #solution to min_z||A_Sz - y||_2^2, where A_S is the submatrix of columns we have currently chosen
'lambda' : True, #the vector of residuals, lambda = A^T(A_Sx-y), where x is the optimal solution to min_z||A_Sz - y||_2^2
#---------------------------------------------------------------
#MCTS parameters
'cpuct': 1, #controls the amount of exploration at each depth of MCTS tree.
'numMCTSSims': 500, #For each move, numMCTSSims is equal to the number of MCTS simulations in finding the next move during self play.
'tempThreshold': 0, #dictates when the MCTS starts returning deterministic polices (vector of 0 and 1's). See Coach.py for more details.
'gamma': 1, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller gamma is, the more likely algorithm is going to choose stopping action earlier(when ||x||_0 is small). gamma enforces how much we want to enforce Ax is close to y. We need gamma large enough!!!
'alpha': 1e-5, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller alpha is, the more weight the algorithm gives in selecting a sparse solution.
'epsilon': 1e-5, #If x is the optimal solution to l2, and the residual of l2 regression ||A_Sx-y||_2^2 is less than epsilon, then the state corresponding to indices S is a terminal state in MCTS.
}
test_loss = np.zeros(alphazero_iterations)
for i in range(alphazero_iterations):
#-------------------------------------------------------------
game_args = Game_args()
matrix_filename = 'sdnormal0.npy'
A = np.load(matrix_filename)
game_args.sensing_matrix = A
nnet = NNetWrapper(args)
model_filepath = os.getcwd() + '/network_checkpoint'
model_filename = 'nnet_checkpoint' + str(i)
nnet.load_checkpoint(model_filepath, model_filename)
new_game = CSGame()
Alphazero = Coach(new_game, nnet, args, game_args)
#--------------------------------------------------------------
#Alphazero now ready for prediction
avgloss_for_NN = 0
any agent.
"""
human_vs_cpu = True
g = HalfchessGame()
# all players
rp = RandomPlayer(g).play
# gp = GreedyOthelloPlayer(g).play
hp = HumanPlayer(g).play
# nnet player
nn = NNet(g)
#nn.load_checkpoint('./temp/','best.pth.tar')
nn.load_checkpoint('./pretrained_models/halfchess', '43it.pth.tar')
args = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts = MCTS(g, nn, args)
nnp = lambda x: np.argmax(mcts.getActionProb(x, temp=1))
if human_vs_cpu:
player1 = hp
else:
n2 = NNet(g)
n2.load_checkpoint('./pretrained_models/halfchess/', '26it_fixed_logic.pth.tar')
args2 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts2 = MCTS(g, n2, args2)
n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
# player2 = n2p # Player 2 is neural network if it's cpu vs cpu.
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
n2 = NNet(g, argsNN)
n2.load_checkpoint('/content/drive/My Drive/temp/','checkpoint_1.pth.tar')
args2 = dotdict({'numMCTSSims': 50, 'cpuct':1.0})
mcts2 = MCTS(g, n2, args2)
n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
"""
for p in range(1, 5):
print("iter:%d" % p)
for i in range(70, 1, -1):
n1 = NNet(g, argsNN)
try:
n1.load_checkpoint('/content/drive/My Drive/model/',
'checkpoint_%d.pth.tar' % i)
except:
print("no model:%d" % i)
continue
args1 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0)) #expoitation
for j in range(70, 1, -1):
if (i <= j): continue
n2 = NNet(g, argsNN)
try:
n2.load_checkpoint('/content/drive/My Drive/model/',
'checkpoint_%d.pth.tar' % j)
except:
print("no model:%d" % j)
def PlayGame():
menu_def = [
['&File', ['&Do nothing', 'E&xit']],
['&Help', '&About...'],
]
# sg.SetOptions(margins=(0,0))
sg.ChangeLookAndFeel('GreenTan')
# create initial board setup
psg_board = copy.deepcopy(initial_board)
# the main board display layout
board_layout = [[sg.T(' ')] + [
sg.T('{}'.format(a), pad=((23, 27), 0), font='Any 13') for a in 'efgh'
]]
# loop though board and create buttons with images
for i in range(8):
row = [sg.T(str(8 - i) + ' ', font='Any 13')]
for j in range(4):
piece_image = images[psg_board[i][j]]
row.append(render_square(piece_image, key=(i, j), location=(i, j)))
row.append(sg.T(str(8 - i) + ' ', font='Any 13'))
board_layout.append(row)
# add the labels across bottom of board
board_layout.append([sg.T(' ')] + [
sg.T('{}'.format(a), pad=((23, 27), 0), font='Any 13') for a in 'efgh'
])
# setup the controls on the right side of screen
openings = ('Any', 'Defense', 'Attack', 'Trap', 'Gambit', 'Counter',
'Sicillian', 'English', 'French', 'Queen\'s openings',
'King\'s Openings', 'Indian Openings')
board_controls = [
[sg.RButton('New Game', key='New Game'),
sg.RButton('Draw')],
[sg.RButton('Resign Game'),
sg.RButton('Set FEN')],
[sg.RButton('Player Odds'),
sg.RButton('Training')],
[sg.Drop(openings), sg.Text('Opening/Style')],
[sg.CBox('Play As White', key='_white_')],
[sg.Text('Move List')],
[
sg.Multiline([],
do_not_clear=True,
autoscroll=True,
size=(15, 10),
key='_movelist_')
],
]
# layouts for the tabs
controls_layout = [[
sg.Text('Performance Parameters', font='_ 20')
], [sg.T('Put stuff like AI engine tuning parms on this tab')]]
statistics_layout = [[sg.Text('Statistics', font=('_ 20'))],
[sg.T('Game statistics go here?')]]
board_tab = [[sg.Column(board_layout)]]
# the main window layout
layout = [[sg.Menu(menu_def, tearoff=False)],
[
sg.TabGroup([[
sg.Tab('Board', board_tab),
sg.Tab('Controls', controls_layout),
sg.Tab('Statistics', statistics_layout)
]],
title_color='red'),
sg.Column(board_controls)
],
[sg.Text('Click anywhere on board for next move', font='_ 14')]]
window = sg.Window('Chess',
default_button_element_size=(12, 1),
auto_size_buttons=False,
icon='kingb.ico').Layout(layout)
g = HalfchessGame.HalfchessGame()
nn = NNet(g)
nn.load_checkpoint(nn_filepath, nn_filename)
args = dotdict({'numMCTSSims': numMCTSSims, 'cpuct': cpuct})
mcts = MCTS(g, nn, args)
nnp = lambda x: np.argmax(mcts.getActionProb(x, temp=temp))
board = g.getInitBoard()
move_count = curPlayer = 1
move_state = move_from = move_to = 0
# ---===--- Loop taking in user input --- #
while g.getGameEnded(board, curPlayer) == 0:
canonicalBoard = g.getCanonicalForm(board, curPlayer)
if curPlayer == human:
# human_player(board)
move_state = 0
while True:
button, value = window.Read()
if button in (None, 'Exit'):
exit()
if button == 'New Game':
sg.Popup(
'You have to restart the program to start a new game... sorry....'
)
break
psg_board = copy.deepcopy(initial_board)
redraw_board(window, psg_board)
move_state = 0
break
if type(button) is tuple:
if move_state == 0:
move_from = button
row, col = move_from
piece = psg_board[row][col] # get the move-from piece
button_square = window.FindElement(key=(row, col))
button_square.Update(button_color=('white', 'red'))
move_state = 1
elif move_state == 1:
move_to = button
row, col = move_to
if move_to == move_from: # cancelled move
color = '#B58863' if (row + col) % 2 else '#F0D9B5'
button_square.Update(button_color=('white', color))
move_state = 0
continue
picked_move = '{}{}{}{}'.format(
'efgh'[move_from[1]], 8 - move_from[0],
'efgh'[move_to[1]], 8 - move_to[0])
action = moveset[picked_move]
valids = g.getValidMoves(canonicalBoard, 1)
if valids[action] != 0:
board, curPlayer = g.getNextState(
board, curPlayer, action)
else:
print('Illegal move')
move_state = 0
color = '#B58863' if (
move_from[0] + move_from[1]) % 2 else '#F0D9B5'
button_square.Update(button_color=('white', color))
continue
psg_board[move_from[0]][move_from[
1]] = BLANK # place blank where piece was
psg_board[row][
col] = piece # place piece in the move-to square
redraw_board(window, psg_board)
move_count += 1
window.FindElement('_movelist_').Update(picked_move +
'\n',
append=True)
break
else:
best_move = nnp(canonicalBoard)
move_str = moveset[best_move]
if curPlayer == -1:
move_str = HalfchessGame.mirrored_move(move_str)
from_col = ord(move_str[0]) - ord('e')
from_row = 8 - int(move_str[1])
to_col = ord(move_str[2]) - ord('e')
to_row = 8 - int(move_str[3])
window.FindElement('_movelist_').Update(move_str + '\n',
append=True)
piece = psg_board[from_row][from_col]
psg_board[from_row][from_col] = BLANK
psg_board[to_row][to_col] = piece
redraw_board(window, psg_board)
board, curPlayer = g.getNextState(board, curPlayer, best_move)
move_count += 1
sg.Popup('Game over!', 'Thank you for playing')
'dropout': 0.3,
'epochs': 10,
'batch_size': 64,
'cuda': torch.cuda.is_available(),
'num_channels': 512,
})
# all players
rp = RandomPlayer(g).play
gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
mmp = MinMaxOthelloPlayer(g).play
# nnet players
n1 = NNet(g, argsNN)
n1.load_checkpoint('./model/', 'best.pth.tar')
args1 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
n2 = NNet(g, argsNN)
n2.load_checkpoint('./model/', 'checkpoint_5.pth.tar')
args2 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts2 = MCTS(g, n2, args2)
n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
arena = Arena.Arena(n1p, mmp, g, [0, 0], display=display)
print(arena.playGames(6, verbose=True))
#arena = Arena.Arena(n2p, mmp, g, [0,0], display=display)
#print(arena.playGames(6, verbose=True))
'gamma': 1, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller gamma is, the more likely algorithm is going to choose stopping action earlier(when ||x||_0 is small). gamma enforces how much we want to enforce Ax is close to y.
#choice of gamma is heavily dependent on the distribution of our signal and the distribution of entries of A. gamma should be apx. bigger than m/||A_Sx^* - y||_2^2, where y = Ax, and x^* is the solution to the l2 regression problem.
'alpha':1e-5, #note that reward for a terminal state is -alpha||x||_0 - gamma*||A_S*x-y||_2^2. The smaller alpha is, the more weight the algorithm gives in selecting a sparse solution.
'epsilon': 1e-5, #If x is the optimal solution to l2, and the residual of l2 regression ||A_Sx-y||_2^2 is less than epsilon, then the state corresponding to indices S is a terminal state in MCTS.
}
#START ALPHAZERO TRAINING:
#Initialize Game_args, nnet, Game, and Alphazero
Game_args = Game_args()
Game = CSGame()
nnet = NNetWrapper(args)
if args['load_nn_model'] == True:
filename = 'best'
nnet.load_checkpoint(args['network_checkpoint'], filename)
if args['skip_rule'] == 'bootstrap':
skip_nnet = NNetWrapper(args)
skip_nnet.load_checkpoint(args['skip_nnet_folder'], args['skip_nnet_filename'])
elif args['skip_rule'] == None:
skip_nnet = nnet
else:
skip_nnet = None
Alphazero_train = Coach(Game, nnet, args, Game_args, skip_nnet)
#FOR TESTING-----------------------------------------------------
weights = Alphazero_train.nnet.nnet.model.get_weights()
'tempThreshold': 40,
'updateThreshold':
0.6, # During arena playoff, new neural net will be accepted if threshold or more of games are won.
'maxlenOfQueue':
300000, # Number of game examples to train the neural networks.
'numMCTSSims': 25, # Number of games moves for MCTS to simulate.
'arenaCompare':
40, # Number of games to play during arena play to determine if new net will be accepted.
'cpuct': 1,
'checkpoint': './temp/',
'load_model': False,
#'load_folder_file': ('/dev/models/8x100x50','best.pth.tar'),
'load_folder_file': ('temp/', 'best.pth.tar'),
'numItersForTrainExamplesHistory': 20,
'dirichlet': 0.03,
})
if __name__ == "__main__":
g = Game()
nnet = HalfchessNNet(g)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
c = Coach(g, nnet, args)
if args.load_model:
print("Load trainExamples from file")
c.loadTrainExamples()
c.learn()
nets = [1, 3, 14, 29, 48, 70, 98]
#nets = [1,14,98]
victories = {}
runner_up = {}
rp = RandomPlayer(g).play
victories[rp] = [0, 0]
runner_up[rp] = [0, 0]
playerpool = [rp]
args = dotdict({'numMCTSSims': 20, 'cpuct': 1.0, 'dirichlet': 0.05})
for i in nets:
nn = NNet(g)
nn.load_checkpoint(filepath, 'checkpoint_%d.pth.tar' % i)
mcts = MCTS(g, nn, args)
def stochastic(x):
pi = mcts.getActionProb(x, temp=1)
return np.random.choice(len(pi), p=pi)
def deterministic(x):
return np.argmax(mcts.getActionProb(x, temp=0))
nnp = deterministic if det_pol else stochastic
victories[nnp] = [i, 0]
runner_up[nnp] = [i, 0]
playerpool.append(nnp)
for it in range(num_tourneys):
def PlayGame():
# menu_def = [['&File', ['&Open PGN File', 'E&xit']],
# ['&Help', '&About...'], ]
# sg.SetOptions(margins=(0,0))
sg.ChangeLookAndFeel('GreenTan')
# create initial board setup
psg_board = copy.deepcopy(initial_board)
# the main board display layout
board_layout = [[sg.T(' ')] + [
sg.T('{}'.format(a), pad=((23, 27), 0), font='Any 13') for a in 'efgh'
]]
# loop though board and create buttons with images
for i in range(8):
row = [sg.T(str(8 - i) + ' ', font='Any 13')]
for j in range(4):
piece_image = images[psg_board[i][j]]
row.append(render_square(piece_image, key=(i, j), location=(i, j)))
row.append(sg.T(str(8 - i) + ' ', font='Any 13'))
board_layout.append(row)
# add the labels across bottom of board
board_layout.append([sg.T(' ')] + [
sg.T('{}'.format(a), pad=((23, 27), 0), font='Any 13') for a in 'efgh'
])
# setup the controls on the right side of screen
# openings = (
# 'Any', 'Defense', 'Attack', 'Trap', 'Gambit', 'Counter', 'Sicillian', 'English', 'French', 'Queen\'s openings',
# 'King\'s Openings', 'Indian Openings')
# board_controls = [[sg.RButton('New Game', key='New Game'), sg.RButton('Draw')],
# [sg.RButton('Resign Game'), sg.RButton('Set FEN')],
# [sg.RButton('Player Odds'), sg.RButton('Training')],
# [sg.Drop(openings), sg.Text('Opening/Style')],
# [sg.CBox('Play As White', key='_white_')],
# [sg.Text('Move List')],
# [sg.Multiline([], do_not_clear=True, autoscroll=True, size=(15, 10), key='_movelist_')],
# ]
# # layouts for the tabs
# controls_layout = [[sg.Text('Performance Parameters', font='_ 20')],
# [sg.T('Put stuff like AI engine tuning parms on this tab')]]
# statistics_layout = [[sg.Text('Statistics', font=('_ 20'))],
# [sg.T('Game statistics go here?')]]
board_tab = [[sg.Column(board_layout)]]
# the main window layout
layout = [ #[sg.Menu(menu_def, tearoff=False)],
[sg.TabGroup([[sg.Tab('Board', board_tab)]])]
]
# sg.Tab('Controls', controls_layout),
# sg.Tab('Statistics', statistics_layout)]], title_color='red'),
#sg.Column(board_controls)],
#[sg.Text('Click anywhere on board for next move', font='_ 14')]]
window = sg.Window('Chess',
default_button_element_size=(12, 1),
auto_size_buttons=False,
icon='kingb.ico').Layout(layout)
g = HalfchessGame.HalfchessGame()
nn = NNet(g)
nn.load_checkpoint(nn_filepath, nn_filename)
args = dotdict({
'numMCTSSims': numMCTSSims,
'cpuct': cpuct,
'dirichlet': 0.5
})
mcts = MCTS(g, nn, args)
def deterministic(x):
return np.argmax(mcts.getActionProb(x, temp=0))
def stochastic(x):
pi = mcts.getActionProb(x, temp=temp)
return np.random.choice(len(pi), p=pi)
nnp = stochastic
nn2 = NNet(g)
nn2.load_checkpoint(nn2_filepath, nn2_filename)
mcts2 = MCTS(g, nn2, args)
def deterministic(x):
return np.argmax(mcts2.getActionProb(x, temp=0))
def stochastic(x):
pi = mcts2.getActionProb(x, temp=temp)
return np.random.choice(len(pi), p=pi)
nnp2 = stochastic
rp = RandomPlayer(g).play
board = g.getInitBoard()
move_count = curPlayer = 1
move_state = move_from = move_to = 0
# ---===--- Loop taking in user input --- #
while g.getGameEnded(board, curPlayer) == 0:
window.Read()
canonicalBoard = g.getCanonicalForm(board, curPlayer)
if curPlayer == p2:
if twonets:
best_move = nnp2(canonicalBoard)
move_str = moveset[best_move]
else:
best_move = rp(canonicalBoard)
move_str = moveset[best_move]
if curPlayer == -1:
move_str = HalfchessGame.mirrored_move(move_str)
from_col = ord(move_str[0]) - ord('e')
from_row = 8 - int(move_str[1])
to_col = ord(move_str[2]) - ord('e')
to_row = 8 - int(move_str[3])
#window.FindElement('_movelist_').Update(move_str + '\n', append=True)
piece = psg_board[from_row][from_col]
psg_board[from_row][from_col] = BLANK
psg_board[to_row][to_col] = piece
redraw_board(window, psg_board)
board, curPlayer = g.getNextState(board, curPlayer, best_move)
move_state = 0
else:
best_move = nnp(canonicalBoard)
move_str = moveset[best_move]
if curPlayer == -1:
move_str = HalfchessGame.mirrored_move(move_str)
from_col = ord(move_str[0]) - ord('e')
from_row = 8 - int(move_str[1])
to_col = ord(move_str[2]) - ord('e')
to_row = 8 - int(move_str[3])
#window.FindElement('_movelist_').Update(move_str + '\n', append=True)
piece = psg_board[from_row][from_col]
psg_board[from_row][from_col] = BLANK
psg_board[to_row][to_col] = piece
redraw_board(window, psg_board)
board, curPlayer = g.getNextState(board, curPlayer, best_move)
move_count += 1
sg.Popup('Game over!', 'Thank you for playing')