def play(self, board):
# display(board)
valid = self.game.getValidMoves(board, 1)
print('hp: possible moves: ', end="")
for i in range(len(valid)):
if valid[i]:
print(i, end=' ')
print('')
while True:
# Python 3.x
a = input()
# Python 2.x
# a = raw_input()
x = 0
for y in a.split(' '):
x = int(y)
a = x if x != -1 else 2 * self.game.n
if a >= 0 and a <= self.game.n and valid[a]:
break
else:
print('hp: Invalid')
select = a
b = Board(6)
b.pieces = np.copy(board)
b.check_board(select, prefix="hp: ")
return a
def play(self, board):
'''
:param board: the current configuration of the board
:return: if more actions have the same value, which is the best one, it returns randomly one action from these
'''
valids = self.game.getValidMoves(board, 1)
candidates = []
for a in range(self.game.getActionSize()):
if valids[a] == 0:
continue
nextBoard, _ = self.game.getNextState(board, 1, a)
score = self.game.getScore(nextBoard, 1)
candidates += [(-score, a)]
candidates.sort()
list = []
max = candidates[0][0]
for i in range(len(candidates)):
if candidates[i][0] == max:
list.append(candidates[i][1])
select = random.choice(list)
b = Board(6)
b.pieces = np.copy(board)
b.check_board(select, prefix="gp: ")
return select
def play(self, board):
args1 = dotdict({'numMCTSSims': args.mcts, 'cpuct': args.cpuct})
mcts1 = MCTS(self.game, self.n1, args1)
actions = mcts1.getActionProb(board, temp=1)
select = np.argmax(actions)
#print('board: ', end="")
#print(board)
#print('action p-values: ' + str(actions))
b = Board(6)
b.pieces = np.copy(board)
b.check_board(select, prefix="nn: ")
return select
def play(self, board):
'''
:param board: the configuration of the board
:return: the action from the tuple (action, score) where this action is the best action detected by alfa-beta
'''
score = self.minimax((board, -1), self.depth, 1, -infinity, +infinity)
print("mp: minmax at depth " + str(self.depth))
select = score[0]
print("mp: select " + str(select))
b = Board(6)
b.pieces = np.copy(board)
b.check_board(select, prefix="mp: ")
return select
def play(self, board):
select = np.random.randint(self.game.getActionSize())
valid = self.game.getValidMoves(board, 1)
print('rp: possible moves: ', end="")
for i in range(len(valid)):
if valid[i]:
print(i, end=' ')
print('')
while valid[select] != 1:
select = np.random.randint(self.game.getActionSize())
print('rp: select ' + str(select))
b = Board(6)
b.pieces = np.copy(board)
b.check_board(select, prefix="rp: ")
return select