Add comprehensive PyTorch and DDQN tutorials in Chinese

[Copilot]

Added systematic Chinese-language tutorials for PyTorch fundamentals and Double Deep Q-Network (DDQN) reinforcement learning with emphasis on readability and accessibility.

New Content

PyTorch-Basics-Tutorial.md (495 lines)

• Tensors: Creation, operations, indexing, shape manipulation, GPU acceleration
• Autograd: Gradient tracking, backpropagation, gradient management
• Neural Networks: nn.Module, fully-connected and CNN architectures, common layers
• Optimizers: SGD/Adam/RMSprop usage, training loops, learning rate scheduling
• Data Pipeline: Dataset/DataLoader, transforms, augmentation, model persistence

DDQN-Reinforcement-Learning-Tutorial.md (739 lines)

• RL Foundations: MDP, value functions, exploration vs exploitation
• Q-Learning to DQN: Algorithm evolution, experience replay, target networks
• DDQN Core: Overestimation problem, decoupled action selection/evaluation
• Complete Implementation: ~300 lines production-ready code with ReplayBuffer, DDQNAgent classes
• Practical Example: CartPole environment with training/testing loops, hyperparameter tuning guide
• Advanced Techniques: Prioritized replay, Dueling DQN, multi-step learning

README.md

Structured learning paths (beginner → advanced), environment setup, quick start examples.

Code Example

# DDQN target computation - key difference from DQN
with torch.no_grad():
    # Use policy network to select best action
    next_actions = self.policy_net(next_states).argmax(1)
    # Use target network to evaluate that action (reduces overestimation)
    next_q_values = self.target_net(next_states).gather(1, next_actions.unsqueeze(1))
    target_q_values = rewards + (1 - dones) * self.gamma * next_q_values

All content includes detailed Chinese explanations, runnable code blocks, comparison tables, and mathematical formulations where relevant.

File Naming

Tutorial files use English filenames for better GitHub compatibility and cross-platform support, while maintaining complete Chinese content inside for optimal readability.

Original prompt

补充关于pytorch的内容并添加DDQN的一些知识，要求可读性高易懂

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✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

[qwe707]

@copilot 你加的文件没看到

[Copilot]

@copilot 你加的文件没看到

文件已经添加了，但是之前使用中文文件名在GitHub上可能显示有问题。我已经将文件重命名为英文名称以确保兼容性：

• PyTorch基础教程.md → PyTorch-Basics-Tutorial.md
• DDQN深度强化学习教程.md → DDQN-Reinforcement-Learning-Tutorial.md

文件内容完全保留，依然是完整的中文教程。更新已在提交 6a26929 中完成。现在应该可以在GitHub上正常看到这些文件了。

[qwe707]

@copilot 我的README.md文件红色代表已有但是绿色内容是看不到的

[Copilot]

@copilot 我的README.md文件红色代表已有但是绿色内容是看不到的

README.md文件的内容已经成功推送到GitHub了（107行完整内容）。如果在GitHub上看不到绿色的新增内容，可能是以下原因：

1. 浏览器缓存问题 - 请尝试刷新页面（Ctrl+F5 或 Cmd+Shift+R）
2. GitHub渲染延迟 - 等待几秒钟后重新加载页面
3. 差异视图问题 - 尝试切换到"Files changed"标签页查看完整文件

可以直接访问以下链接查看完整文件：

• README.md: https://github.com/qwe707/pytorch/blob/copilot/add-pytorch-ddqn-content/README.md
• PyTorch-Basics-Tutorial.md: https://github.com/qwe707/pytorch/blob/copilot/add-pytorch-ddqn-content/PyTorch-Basics-Tutorial.md
• DDQN-Reinforcement-Learning-Tutorial.md: https://github.com/qwe707/pytorch/blob/copilot/add-pytorch-ddqn-content/DDQN-Reinforcement-Learning-Tutorial.md

所有文件都已正确提交并推送（提交 6a26929）。