def getMove(self, state):
# rotate the board for current player
player = s.getCurrentPlayer(state)
if player != 0:
flip = True
board = s.flipBoard(state)
else:
flip = False
board = state
# get output of neural network
fd = {self.x: [self.makeInputVector(board[:14])], self.keep_prob: 1.0}
y = self.sess.run(self.y, fd)
# y is a list containing a single output vector
# y == [[0.0108906 0.1377293 0.370027 0.2287382 0.0950692 0.1575449]]
scores = list(y[0])
# we only want to pick from legal moves (the nn will learn these
# eventually, but we're helping him with this constraint)
legalMoves = s.getLegalMoves(board)
move = self.chooseMoveRandomly(scores, legalMoves)
if len([x for x in scores if math.isnan(x)]) > 0:
logger.error(self.name + " returned NaN!")
# if we rotated the board before, rotate it back
if flip:
move = s.flipMove(move, player)
return move
def play_one_game(players, lucky):
game = s.init()
done = False
moves = []
while not done:
# do move for someone
player = s.getCurrentPlayer(game)
if needRandomMove(len(moves)):
move = lucky.move(game)
else:
move = players[player]['ai'].move(game)
if move is None:
logger.error("null move! ", game)
mt = [s.flipBoardCurrentPlayer(game), s.flipMove(move, player), player]
moves.append(mt)
game = s.doMove(game, move)
done = s.isGameOver(game)
winner = s.getWinner(game)
score = s.getScore(game)
# make training set with move, gamestate, and 1 for win, 0 for lose
trainingset = [
d[0:2] + [int(winner == d[2])] + list(score)[::1 - d[2] * 2]
for d in moves
]
for move in trainingset:
results.info(move)
i = 0
for p in players:
isWinner = (1 if i == winner else 0)
p['ai'].gameOver(isWinner)
p['wins'] += isWinner
i += 1
return (winner, trainingset)
def play_game(players):
game = s.init()
done = False
moves = []
while not done:
# do move for someone
player = s.getCurrentPlayer(game)
move = players[player]['ai'].move(game)
if move is None:
print("null move! ", game)
mt = {
"move": s.flipMove(move, player),
"board": s.flipBoardCurrentPlayer(game),
"player": player,
"name": players[player]['module'].__name__
}
moves.append(mt)
game = s.doMove(game, move)
done = s.isGameOver(game)
winner = s.getWinner(game)
# make training set with move, gamestate, and 1 for win, 0 for lose
trainingset = [dict(d, winner=int(winner == d['player'])) for d in moves]
i = 0
for p in players:
p['ai'].gameOver(i == winner)
i += 1
return (winner, trainingset)
def move(self, state):
player = s.getCurrentPlayer(state)
if player != 0:
flip = True
board = s.flipBoard(state)
else:
flip = False
board = state
move = self.nn.getMove(board)
if flip:
move = s.flipMove(move, player)
return move
def move(self, state):
# rotate the board for current player
player = s.getCurrentPlayer(state)
if player != 0:
flip = True
board = s.flipBoard(state)
else:
flip = False
board = state
# pick best move
vector = moveToVector(board)
bestmove = None
bestscore = 0
for i in range(6):
move = 5 - i
if vector[move] > bestscore:
bestmove = move
bestscore = vector[move]
# flip move
if flip:
bestmove = s.flipMove(bestmove, player)
return bestmove