人工智能领域最新研究进展

import gym
import numpy as np
import random
from tqdm import tqdm
 
# 创建环境
env = gym.make("FrozenLake-v1", is_slippery=False)
 
# 初始化 Q 表
Q = np.zeros((env.observation_space.n, env.action_space.n))
 
# 超参数
alpha = 0.8        # 学习率
gamma = 0.95       # 折扣因子
epsilon = 0.1      # 探索率
num_episodes = 2000
 
# 训练 Q-learning 算法
for episode in tqdm(range(num_episodes)):
    state = env.reset()  # 重置环境状态
    done = False
 
    while not done:
        if random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()  # 随机选择动作（探索）
        else:
            action = np.argmax(Q[state, :])    # 利用当前策略选择动作（利用）
 
        next_state, reward, done, _ = env.step(action)
 
        # 更新 Q 表
        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])
 
        state = next_state
 
print("训练完成")
 
# 测试训练好的策略
def run_episode(env, Q):
    state = env.reset()
    total_reward = 0
    done = False
 
    while not done:
        env.render()
        action = np.argmax(Q[state, :])
        state, reward, done, _ = env.step(action)
        total_reward += reward
 
    print(f"总奖励: {total_reward}")
 
# 运行一个测试回合
run_episode(env, Q)
 
# 关闭环境
env.close()
 
音频生成
from gtts import gTTS
import os
 
def text_to_speech(text, lang='zh'):
    tts = gTTS(text=text, lang=lang)
    tts.save("output.mp3")
    os.system("start output.mp3")  # 在Windows上播放音频
 
# 示例调用
text = "你好，欢迎使用人工智能生成内容的示例。"
text_to_speech(text)