Reinforcement Learning
强化学习研究智能体通过与环境交互、试错优化策略以最大化累积回报的学习范式。课程内容包括马尔可夫决策过程、值迭代与策略迭代、蒙特卡洛方法、时序差分学习（Q-Learning、SARSA）、Deep Q-Network、策略梯度方法（REINFORCE、PPO）、Actor-Critic框架及多智能体强化学习。学生将掌握序列决策的核心算法，应用于游戏AI、机器人控制、推荐系统等场景。
Reinforcement Learning studies how agents learn optimal policies through environment interaction and trial-and-error to maximize cumulative reward. Topics include Markov decision processes, value and policy iteration, Monte Carlo methods, temporal difference learning (Q-Learning, SARSA), Deep Q-Network, policy gradient methods (REINFORCE, PPO), Actor-Critic frameworks, and multi-agent RL. Students master sequential decision-making algorithms applicable to game AI, robotic control, and recommender systems.
选择经典游戏环境（如CartPole、Atari或围棋），实现DQN、PPO或Actor-Critic等强化学习算法，训练智能体掌握最优策略。提交训练代码、学习曲线分析及智能体演示视频。
Select a classic game environment (e.g., CartPole, Atari or Go), implement RL algorithms such as DQN, PPO or Actor-Critic, and train the agent to master optimal strategies. Submit training code, learning curve analysis, and agent demo video.