从跟跑到领跑：AIGC时代国产游戏的崛起与展望

 import tensorflow as tf

    
 from tensorflow.keras.layers import Dense, Flatten, Reshape
    
 from tensorflow.keras.models import Sequential
    
  
    
 # 定义生成器模型
    
 def build_generator():
    
     model = Sequential()
    
     model.add(Dense(128, input_dim=100, activation='relu'))
    
     model.add(Dense(784, activation='sigmoid'))
    
     model.add(Reshape((28, 28, 1)))
    
     return model
    
  
    
 # 定义判别器模型
    
 def build_discriminator():
    
     model = Sequential()
    
     model.add(Flatten(input_shape=(28, 28, 1)))
    
     model.add(Dense(128, activation='relu'))
    
     model.add(Dense(1, activation='sigmoid'))
    
     return model
    
  
    
 # 定义GAN模型
    
 def build_gan(generator, discriminator):
    
     model = Sequential()
    
     model.add(generator)
    
     model.add(discriminator)
    
     return model
    
  
    
 # 编译模型
    
 generator = build_generator()
    
 discriminator = build_discriminator()
    
 discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
  
    
 gan = build_gan(generator, discriminator)
    
 gan.compile(optimizer='adam', loss='binary_crossentropy')
    
  
    
 # 训练GAN
    
 def train_gan(epochs=10000, batch_size=64):
    
     for epoch in range(epochs):
    
     noise = tf.random.normal([batch_size, 100])
    
     generated_images = generator(noise, training=False)
    
  
    
     # 判别器训练
    
     real_images = tf.random.normal([batch_size, 28, 28, 1])
    
     real_labels = tf.ones((batch_size, 1))
    
     fake_labels = tf.zeros((batch_size, 1))
    
  
    
     with tf.GradientTape() as tape:
    
         real_output = discriminator(real_images, training=True)
    
         fake_output = discriminator(generated_images, training=True)
    
         real_loss = tf.keras.losses.binary_crossentropy(real_labels, real_output)
    
         fake_loss = tf.keras.losses.binary_crossentropy(fake_labels, fake_output)
    
         d_loss = real_loss + fake_loss
    
  
    
     gradients = tape.gradient(d_loss, discriminator.trainable_variables)
    
     discriminator.optimizer.apply_gradients(zip(gradients, discriminator.trainable_variables))
    
  
    
     # 生成器训练
    
     noise = tf.random.normal([batch_size, 100])
    
     with tf.GradientTape() as tape:
    
         generated_images = generator(noise, training=True)
    
         fake_output = discriminator(generated_images, training=False)
    
         g_loss = tf.keras.losses.binary_crossentropy(real_labels, fake_output)
    
  
    
     gradients = tape.gradient(g_loss, generator.trainable_variables)
    
     gan.optimizer.apply_gradients(zip(gradients, generator.trainable_variables))
    
  
    
     if epoch % 1000 == 0:
    
         print(f'Epoch {epoch}, Discriminator Loss: {d_loss.numpy()}, Generator Loss: {g_loss.numpy()}')
    
  
    
 train_gan()

 from transformers import GPT2LMHeadModel, GPT2Tokenizer

    
  
    
 # 加载预训练模型和分词器
    
 model_name = 'gpt2'
    
 model = GPT2LMHeadModel.from_pretrained(model_name)
    
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    
  
    
 # 生成游戏剧情
    
 def generate_story(prompt, max_length=200):
    
     input_ids = tokenizer.encode(prompt, return_tensors='pt')
    
     output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, temperature=0.7)
    
     story = tokenizer.decode(output[0], skip_special_tokens=True)
    
     return story
    
  
    
 prompt = "在一个神秘的魔法世界里，"
    
 story = generate_story(prompt)
    
 print(story)

 import numpy as np

    
  
    
 # 环境设置
    
 n_actions = 4  # 例如：上、下、左、右
    
 n_states = 16  # 16个状态
    
 q_table = np.zeros((n_states, n_actions))
    
 learning_rate = 0.1
    
 discount_factor = 0.9
    
 epsilon = 0.1
    
  
    
 # 简单环境模拟
    
 def take_action(state, action):
    
     # 示例环境逻辑
    
     next_state = (state + action) % n_states
    
     reward = 1 if next_state == 0 else 0
    
     return next_state, reward
    
  
    
 # 训练NPC
    
 def train_npc(epochs=1000):
    
     global q_table
    
     for _ in range(epochs):
    
     state = np.random.randint(n_states)
    
     while True:
    
         if np.random.rand() < epsilon:
    
             action = np.random.randint(n_actions)
    
         else:
    
             action = np.argmax(q_table[state])
    
         
    
         next_state, reward = take_action(state, action)
    
         best_next_action = np.argmax(q_table[next_state])
    
         td_target = reward + discount_factor * q_table[next_state, best_next_action]
    
         td_error = td_target - q_table[state, action]
    
         q_table[state, action] += learning_rate * td_error
    
         
    
         state = next_state
    
         if state == 0:
    
             break
    
  
    
 train_npc()
    
 print(q_table)

 from selenium import webdriver

    
 from selenium.webdriver.common.keys import Keys
    
 import time
    
  
    
 # 配置WebDriver
    
 driver = webdriver.Chrome()
    
 driver.get("http://your-game-url.com")
    
  
    
 # 模拟玩家操作
    
 def test_game():
    
     # 登录游戏
    
     login_button = driver.find_element_by_id('login-button')
    
     login_button.click()
    
     time.sleep(2)
    
     
    
     # 输入用户名和密码
    
     username_input = driver.find_element_by_id('username')
    
     password_input = driver.find_element_by_id('password')
    
     username_input.send_keys('test_user')
    
     password_input.send_keys('test_password')
    
     login_button = driver.find_element_by_id('submit-button')
    
     login_button.click()
    
     time.sleep(2)
    
     
    
     # 执行游戏操作
    
     move_button = driver.find_element_by_id('move-button')
    
     move_button.click()
    
     time.sleep(1)
    
     
    
     # 验证游戏状态
    
     status = driver.find_element_by_id('status').text
    
     assert status == 'Expected Status'
    
  
    
 test_game()
    
 driver.quit()