// the find
wangshusen/DRL
Deep Reinforcement Learning
A lecture slide deck and notes collection for deep reinforcement learning, covering Q-learning, policy gradient, actor-critic, DQN variants, multi-agent RL, and continuous action spaces. It's course material from a Chinese university professor, not a library or framework — you're getting PDFs and a lecture note, nothing you can run. Target audience is students or self-learners who want a structured academic introduction to DRL.
The topic coverage is solid for an introductory course: TD learning, DQN improvements (double DQN, dueling networks, prioritized replay), policy gradient with baseline, and A2C are all there. The progression from basics through advanced topics is logical and well-sequenced. Slides are paired with YouTube lectures for each topic, so you're not left decoding static PDFs alone. The multi-agent section is a genuine differentiator — most beginner DRL resources skip MARL entirely.
There is zero code in this repo. No implementations, no Jupyter notebooks, no gym environments — just PDFs. Last commit was December 2022, so PPO, SAC, and anything from the post-ChatGPT RL renaissance (RLHF, GRPO) are absent. The lectures are in Chinese, which cuts off a large part of the audience the slides alone would serve. Imitation learning and partial observation sections are listed but marked incomplete, so the curriculum has visible gaps.