AIGC 游戏在 AIGC 领域的关卡设计思路

    import numpy as np
    from collections import defaultdict
    
    class WFCSolver:
    def __init__(self, sample, output_size):
        self.sample = sample
        self.output_size = output_size
        self.pattern_size = 3
        self.patterns = self._extract_patterns()
        self.rules = self._create_rules()
        
    def _extract_patterns(self):
        patterns = defaultdict(int)
        height, width = self.sample.shape
        
        for y in range(height - self.pattern_size + 1):
            for x in range(width - self.pattern_size + 1):
                pattern = tuple(tuple(
                    self.sample[y + dy, x + dx] 
                    for dx in range(self.pattern_size)
                ) for dy in range(self.pattern_size))
                patterns[pattern] += 1
                
        return patterns
    
    def _create_rules(self):
        rules = defaultdict(set)
        for pattern in self.patterns:
            for direction in ['up', 'down', 'left', 'right']:
                adj_patterns = self._find_compatible_patterns(pattern, direction)
                rules[(pattern, direction)] = adj_patterns
        return rules
    
    def generate(self):
        output = np.zeros(self.output_size, dtype=int)
        # 初始化所有位置为所有可能的模式
        possibilities = {
            (y, x): set(self.patterns.keys())
            for y in range(self.output_size[0])
            for x in range(self.output_size[1])
        }
        
        while not self._is_fully_collapsed(possibilities):
            # 选择熵最小的位置
            pos = self._min_entropy_position(possibilities)
            if not pos:
                break
                
            # 坍缩该位置
            self._collapse_position(possibilities, pos)
            
            # 传播约束
            self._propagate_constraints(possibilities, pos)
            
        return output
    
    # 其他辅助方法...
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/L1F80tdlowJQ4IpGAKH5caqsPVCe.png)

    import tensorflow as tf
    from tensorflow.keras import layers
    
    def build_generator(latent_dim):
    model = tf.keras.Sequential([
        layers.Dense(8*8*256, use_bias=False, input_shape=(latent_dim,)),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        
        layers.Reshape((8, 8, 256)),
        
        layers.Conv2DTranspose(128, (5,5), strides=(1,1), padding='same', use_bias=False),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        
        layers.Conv2DTranspose(64, (5,5), strides=(2,2), padding='same', use_bias=False),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        
        layers.Conv2DTranspose(1, (5,5), strides=(2,2), padding='same', use_bias=False, activation='tanh')
    ])
    return model
    
    def build_discriminator():
    model = tf.keras.Sequential([
        layers.Conv2D(64, (5,5), strides=(2,2), padding='same', input_shape=[32,32,1]),
        layers.LeakyReLU(),
        layers.Dropout(0.3),
        
        layers.Conv2D(128, (5,5), strides=(2,2), padding='same'),
        layers.LeakyReLU(),
        layers.Dropout(0.3),
        
        layers.Flatten(),
        layers.Dense(1)
    ])
    return model
    
    # 训练过程
    cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
    
    def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    return real_loss + fake_loss
    
    def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/TnNcoXP9QuRJ3d08LsjCHBltyAkF.png)

    import gym
    from stable_baselines3 import PPO
    from stable_baselines3.common.env_util import make_vec_env
    
    class LevelDesignEnv(gym.Env):
    def __init__(self, base_level):
        super(LevelDesignEnv, self).__init__()
        self.base_level = base_level
        self.current_level = base_level.copy()
        self.action_space = gym.spaces.Discrete(4)  # 四种修改操作
        self.observation_space = gym.spaces.Box(
            low=0, high=1, shape=(32,32), dtype=np.float32)
        
    def step(self, action):
        # 应用关卡修改
        self._apply_action(action)
        
        # 模拟玩家体验
        playability_score = self._simulate_playability()
        engagement_score = self._simulate_engagement()
        
        reward = playability_score * 0.7 + engagement_score * 0.3
        done = playability_score > 0.9 and engagement_score > 0.8
        
        return self.current_level, reward, done, {}
    
    def reset(self):
        self.current_level = self.base_level.copy()
        return self.current_level
    
    # 其他方法...
    
    # 训练RL模型
    env = make_vec_env(lambda: LevelDesignEnv(base_level), n_envs=4)
    model = PPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=100000)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/v21N4tpCM6lYOIZfbyJji9u3h0xm.png)

    pip install tensorflow gym stable-baselines3 numpy matplotlib
    
    
    bash

    class HybridGenerator:
    def __init__(self, gan_model, wfc_solver):
        self.gan = gan_model
        self.wfc = wfc_solver
        
    def generate(self, latent_vector=None):
        # 使用GAN生成初始布局
        if latent_vector is None:
            latent_vector = np.random.normal(size=(1, self.gan.latent_dim))
        rough_level = self.gan.generate(latent_vector)
        
        # 使用WFC细化布局
        refined_level = self.wfc.generate(rough_level)
        
        # 后处理
        final_level = self._post_process(refined_level)
        
        return final_level
    
    def _post_process(self, level):
        # 确保起点和终点连通
        if not self._is_connected(level):
            level = self._connect_paths(level)
            
        # 平衡资源分布
        level = self._balance_resources(level)
        
        return level
    
    # 其他辅助方法...
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/9NvL4KxFAlRn8Beq6XptbgT3SO5V.png)

    class LevelEvaluator:
    def __init__(self, simulator):
        self.simulator = simulator
        
    def evaluate(self, level):
        metrics = {
            'playability': self._calc_playability(level),
            'difficulty_curve': self._calc_difficulty_curve(level),
            'resource_balance': self._calc_resource_balance(level),
            'aesthetic_score': self._calc_aesthetic_score(level)
        }
        
        overall_score = (
            0.4 * metrics['playability'] +
            0.3 * metrics['difficulty_curve'] +
            0.2 * metrics['resource_balance'] +
            0.1 * metrics['aesthetic_score']
        )
        
        return overall_score, metrics
    
    def _calc_playability(self, level):
        # 模拟100次游戏
        successes = 0
        for _ in range(100):
            result = self.simulator.run(level)
            if result['success']:
                successes += 1
        return successes / 100
    
    # 其他评估方法...
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/r5HZnwkFOjLV9iU7hTb4KPSctNMX.png)