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-...
(object): ''' class is only here to create a namespace ''' def breakthrough(self): # one channel, sharing black/white control = binary_control("control", "black", "white") cell_holds = simple_board_channels("cellHolds", ["white", "black"]) return GameDesc("breakthrough", "1 ...
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...
def select_action(config: AlphaZeroConfig, game: Game, root: Node): visit_counts = [(child.visit_count, action) for action, child in root.children.iteritems()] if len(game.history) < config.num_sampling_moves: _, action = softmax_sample(visit_counts) else: _, action = max(visit_...
(grad,0)# Keep in mind that the Variables are redundant because they are shared# across towers. So .. we will just return the first tower's pointer to# the Variable.v=grad_and_vars[0][1]grad_and_var=(grad,v)average_grads.append(grad_and_var)returnaverage_gradsdefadd_grad_to_list...
sum(axis=1) for i in range(len(player_pieces) - self.win_length + 2) ] return max([x.max() for x in run_lengths]) >= self.win_length def __str__(self): return str(self.np_pieces) 2.2.2 游戏设计 In [ ] class Connect4Game(object): """ Connect4 Game class implementing...
13 game: result,14 id: game_id15 })16 game_id += 1二是训练(Training) ,拿新鲜生成的数据,来改进当前的神经网络。1deftrain():2 criterion = AlphaLoss()3 dataset = SelfPlayDataset()4 player, checkpoint = load_player(current_time, loaded_version)5 optimizer = create_optimizer(player, lr,...
def supervise_actions(user): while True: activity = user.fetch_activity_record() if is_abnormies(activity): notify_security_force(activity) To sum up, ZTNA offers a far-reaching protective cover against a spectrum of cyber threats. By imposing stringent access regulations, deploying multi...
(stimulus,dtype=np.float32),batch_count, )returnresponse_container@ti.kerneldef_gpu_convolve(response_container:ti.types.ndarray(dtype=ti.f32,ndim=1),kernel_slab:ti.types.ndarray(dtype=ti.f32,ndim=3),stimulus:ti.types.ndarray(dtype=ti.f32,ndim=3),batch_count:int, ):forx_idx,y_idx,...
def__init__(self,init_model=None,transfer_model=None): self.resnet_block=19# num of block structures in resnet # params of the board and the game self.board_width=11 self.board_height=11 self.n_in_row=5 self.board=Board(width=self.board_width, ...