def do_move(self, move): self.game_start = True # 游戏开始 self.action_count += 1 # 移动次数加1 move_action = move_id2move_action[move] start_y, start_x = int(move_action[0]), int(move_action[1]) end_y, end_x = int...
[:, :] = 1.0 return _current_state # 根据move对棋盘状态做出改变 def do_move(self, move): self.game_start = True # 游戏开始 self.action_count += 1 # 移动次数加1 move_action = move_id2move_action[move] start_y, start_x = int(move_action[0]), int(move_action[1]) end_y, ...
Zero-Sum Game就是听起来高大上的零和游戏,或者零和博弈。指一方的所得正是另一方的所失,变化净额永远是零。通俗点就是“不是你死就是我活”。 相对应的双赢游戏是:win-win game。 例句 They believe they're playing a zero-...
Note: This result in fact applies to any reward function of a general-sum POSG with any number of agents (here N), e.g., to a Dec-POMDP. The following proof handles the general case (with {\varvec{\beta }}_\tau {\mathop {=}\limits ^{\tiny {\text {def}}}\langle \beta ^...
This article presents a short research report on the relationship between perceived antagonism in social relations measured using the Belief in a Zero-Sum Game (BZSG) scale, life satisfaction, and positive and negative affect. Given that individuals who believe that life is like a zero-sum game...
defsearch(s,game,nnet):ifgame.gameEnded(s):return-game.gameReward(s)ifsnotinvisited:visited.add(s)P[s],v=nnet.predict(s)return-vmax_u,best_a=-float("inf"),-1foraingame.getValidActions(s):u=Q[s][a]+c_puct*P[s][a]*sqrt(sum(N[s]))/(1+N[s][a])ifu>max_u:max_u=ubes...
next moveforthe given game board."""returnself._get_action(copy.deepcopy(board.connect_n_game))[0]def_get_action(self,game:ConnectNGame)->Tuple[MoveWithProb]:epsilon=0.25avail_pos=game.get_avail_pos()move_probs:ActionProbs=np.zeros(game.board_size*game.board_size)assertlen(avail_pos)...
def is_game_over(self, player=None): x, y = self.c_action // self.size, self.c_action % self.size if player is None: player = self.c_player for i in range(x - 4, x + 5): if self._get_piece(i, y) == self._get_piece(i + 1, y) == self._get_piece(i + 2, ...
done,winner=self.game_end()reward=0ifdone:ifwinner==self.current_player:reward=1else:reward=-1self.current_player=(self.players[0]ifself.current_player==self.players[1]elseself.players[1])# update stateobs=self.current_state()returnobs,reward,done,self.infodefreset(self):ifboard_width<n...
dnoise) def __str__(self): return "MCTS" 4.实现自博弈过程 实现自博弈训练,基于同一个神经网络初始化对弈双方棋手,对弈过程中双方棋手每下一步前均采用MCTS搜索最优下子策略,每次自博弈一局结束后保存棋局。 # Self-play class Game(object): def __init__(self, white, black, verbose=True): self...