backpropagate

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http://blog.youkuaiyun.com/celerychen2009/article/details/8964753

转载于:https://www.cnblogs.com/hSheng/p/3541200.html

以下是一个针对2048游戏AI的Python代码框架,包含您要求的各个模块。由于篇幅限制,这里给出关键结构示意,实际实现需要补充细节: ```python import numpy as np from collections import deque import heapq from sklearn.ensemble import RandomForestClassifier # -------------------- # 基础结构 # -------------------- class Game: def __init__(self): self.grid = np.zeros((4,4), dtype=int) self.add_new_tile() self.add_new_tile() def add_new_tile(self): # 在空白位置随机添加2(90%)或4(10%) pass def move(self, direction): # 处理移动逻辑,返回是否有效移动 pass def is_game_over(self): # 检查是否无法移动 pass # -------------------- # 搜索算法基类 # -------------------- class AIStrategy: def search(self, game): raise NotImplementedError # -------------------- # a. 朴素搜索算法 # -------------------- class NaiveSearch(AIStrategy): def __init__(self, depth=2): self.search_depth = depth # 搜索深度限制 def evaluate_state(self, grid): # 简单评估函数:空格数 + 最大数值 return np.count_nonzero(grid == 0) + np.max(grid) def search(self, game): best_score = -np.inf best_move = None for direction in ['up', 'down', 'left', 'right']: # 模拟移动 new_game = simulate_move(game, direction) if new_game is None: continue # 递归搜索(示例为深度优先) current_score = self.dfs(new_game, depth=self.search_depth) if current_score > best_score: best_score = current_score best_move = direction return best_move def dfs(self, game, depth): if depth == 0: return self.evaluate_state(game.grid) max_score = -np.inf for dir in possible_moves(game): new_game = simulate_move(game, dir) score = self.dfs(new_game, depth-1) max_score = max(max_score, score) return max_score # -------------------- # b. 启发式搜索 # -------------------- class HeuristicSearch(NaiveSearch): def evaluate_state(self, grid): # 改进的启发式评估函数 empty_cells = np.count_nonzero(grid == 0) monotonicity = self.calculate_monotonicity(grid) merge_potential = self.find_merge_potential(grid) return empty_cells*10 + monotonicity*5 + merge_potential*3 def calculate_monotonicity(self, grid): # 计算行列单调性得分 pass def find_merge_potential(self, grid): # 统计相邻相同块的数量 pass # -------------------- # c. 机器学习辅助 # -------------------- class MLEnhancedSearch(HeuristicSearch): def __init__(self, model_path): self.model = self.load_model(model_path) def extract_features(self, grid): # 特征工程 features = [ np.count_nonzero(grid == 0), # 空格数 np.max(grid), # 最大数值 self.find_adjacent_pairs(grid), # 相邻相同块数 self.calculate_entropy(grid) # 数值分布熵 ] return np.array(features).reshape(1,-1) def search(self, game): # 使用模型筛选高概率方向 candidates = [] for dir in possible_moves(game): sim_game = simulate_move(game, dir) features = self.extract_features(sim_game.grid) prob = self.model.predict_proba(features)[0][1] # 预测成功概率 heapq.heappush(candidates, (-prob, dir)) # 最大堆 return candidates[0][1] if candidates else None # -------------------- # d. 蒙特卡洛树搜索(示意) # -------------------- class MCTSNode: def __init__(self, game_state, parent=None): self.game_state = game_state self.parent = parent self.children = [] self.visits = 0 self.wins = 0 class MCTSSearch(AIStrategy): def __init__(self, iterations=1000): self.iterations = iterations def search(self, root_game): root_node = MCTSNode(root_game) for _ in range(self.iterations): node = self.select(root_node) result = self.simulate(node.game_state) self.backpropagate(node, result) return self.choose_best_move(root_node) def select(self, node): # 根据UCT选择节点 pass def simulate(self, game): # 随机模拟到终局 pass def backpropagate(self, node, result): # 更新节点统计 pass # -------------------- # 辅助函数 # -------------------- def simulate_move(game, direction): # 创建新游戏状态副本并执行移动 pass def possible_moves(game): # 返回所有合法移动方向 pass if __name__ == "__main__": # 使用示例 game = Game() # 选择算法 # ai = NaiveSearch(depth=3) # ai = HeuristicSearch() # ai = MLEnhancedSearch("model.pkl") ai = MCTSSearch(iterations=1000) while not game.is_game_over(): move = ai.search(game) game.move(move) game.add_new_tile() ``` ### 关键实现要点: 1. **状态表示**: - 使用4x4 numpy数组表示游戏状态 - 移动时需要深度复制状态避免污染 2. **性能优化**: - 使用位运算加速状态操作(参考2048优化实现) - 缓存重复状态评估结果 - 并行化蒙特卡洛模拟 3. **机器学习集成**: - 需要预先训练模型(示例使用随机森林) - 特征工程是关键,可考虑: - 行/列单调性 - 数值梯度分布 - 潜在合并机会 - 角落最大值的保持能力 4. **MCTS实现细节**: - 需要处理随机块生成(90%概率生成2) - UCT平衡参数C需要调优 - 快速模拟策略(例如使用启发式代替随机移动) ### 推荐学习资源: 1. **算法实现**: - 《Python人工智能项目实战》第5章(游戏AI) - GitHub热门2048 AI项目(如ovolve/2048-ai) 2. **MCTS优化**: - 论文"A Survey of Monte Carlo Tree Search Methods" - OpenAI Gym 2048环境实现 3. **机器学习集成**: - Kaggle竞赛"2048 Reinforcement Learning" - 使用PyTorch实现DQN的教程 4. **性能优化**: - Numpy高效编程技巧 - Cython加速关键循环 建议从朴素搜索开始逐步实现,每个步骤都进行性能测试和效果评估。完整实现可能需要2000+行代码,但核心算法框架如上述所示。 其中的机器学习和蒙特卡洛部分能详细解释一下吗
05-09
运行以下Python代码:import torchimport torch.nn as nnimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.utils.data import DataLoaderfrom torch.autograd import Variableclass Generator(nn.Module): def __init__(self, input_dim, output_dim, num_filters): super(Generator, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters), nn.ReLU(), nn.Linear(num_filters, num_filters*2), nn.ReLU(), nn.Linear(num_filters*2, num_filters*4), nn.ReLU(), nn.Linear(num_filters*4, output_dim), nn.Tanh() ) def forward(self, x): x = self.net(x) return xclass Discriminator(nn.Module): def __init__(self, input_dim, num_filters): super(Discriminator, self).__init__() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters*4), nn.LeakyReLU(0.2), nn.Linear(num_filters*4, num_filters*2), nn.LeakyReLU(0.2), nn.Linear(num_filters*2, num_filters), nn.LeakyReLU(0.2), nn.Linear(num_filters, 1), nn.Sigmoid() ) def forward(self, x): x = self.net(x) return xclass ConditionalGAN(object): def __init__(self, input_dim, output_dim, num_filters, learning_rate): self.generator = Generator(input_dim, output_dim, num_filters) self.discriminator = Discriminator(input_dim+1, num_filters) self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate) self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate) def train(self, data_loader, num_epochs): for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(data_loader): # Train discriminator with real data real_inputs = Variable(inputs) real_labels = Variable(labels) real_labels = real_labels.view(real_labels.size(0), 1) real_inputs = torch.cat((real_inputs, real_labels), 1) real_outputs = self.discriminator(real_inputs) real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size())) # Train discriminator with fake data noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10)) fake_labels = fake_labels.view(fake_labels.size(0), 1) fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1)) fake_inputs = torch.cat((fake_inputs, fake_labels), 1) fake_outputs = self.discriminator(fake_inputs) fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size())) # Backpropagate and update weights for discriminator discriminator_loss = real_loss + fake_loss self.discriminator.zero_grad() discriminator_loss.backward() self.optimizer_D.step() # Train generator noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,
02-17
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