class AlphaGoPlayer():
def __init__(self, _, seed, player):
self.game = GoGame(13, 7.5) # THe Go Game class
#THERE IS NO init state
self.board = self.game.get_starting_board()
self.seed = seed
self.player = -1 if player == 1 else 1
self.args = parse_args()
self.nnet = NetTrainer(self.game, self.args)
self.nnet.load_checkpoint(self.args.best_model_path)
self.mct = MCT(self.nnet, self.game, self.args, noise=False)
def get_action(self, _, opponent_action):
if opponent_action != -1: # MEANS
self.board = self.game.get_next_state(self.board, -1 * self.player,
opponent_action)
self.board.set_move_num(0)
action_probs = self.mct.actionProb(self.board, self.player, 0)
self.board.set_move_num(-1)
best_action = np.argmax(action_probs)
self.board = self.game.get_next_state(self.board, self.player,
best_action)
return best_action
import random
from go_game import GoGame
import numpy
import sys
numpy.set_printoptions(threshold=sys.maxsize)
if __name__ == "__main__":
game = GoGame(13, 5.5)
board = game.get_starting_board()
player = -1
while True:
if game.get_game_ended(board, player):
break
actions = game.get_valid_moves(board, player)
actions[-1] = 0
selected_action = None
possible_actions = []
for action, indicator in enumerate(actions):
if indicator == 1:
possible_actions.append(action)
if len(possible_actions) > 0:
selected_action = random.choice(possible_actions)
else:
selected_action = game.get_action_space_size() - 1
board = game.get_next_state(board, player, selected_action)