Phase 09: 强化学习

本阶段包含 12 课时。

原始课程来源：AI Engineering from Scratch (MIT License)

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• MDPs, States, Actions & Rewards
• Dynamic Programming — Policy Iteration & Value Iteration
• Monte Carlo Methods — Learning from Complete Episodes
• Temporal Difference — Q-Learning & SARSA
• Deep Q-Networks (DQN)
• Policy Gradient — REINFORCE from Scratch
• Actor-Critic — A2C and A3C
• Proximal Policy Optimization (PPO)
• Reward Modeling & RLHF
• Multi-Agent RL
• Sim-to-Real Transfer
• RL for Games — AlphaZero, MuZero, and the LLM-Reasoning Era