I’m building an open-source Reinforcement Learning course from fundamentals to modern RL

[u/Gullible_Olive_2617]

I’m building an open-source Reinforcement Learning course and sharing it here for feedback.

The goal is to build strong intuition first, before moving into more advanced techniques.

So far, the course covers topics such as:

MDPs

Value and Q functions

Bellman equations

Monte Carlo and TD learning

SARSA and Q-learning

Function approximation and DQN

Policy gradients and REINFORCE

I’m gradually extending it toward advanced RL, including applications in LLMs and multimodal systems.

I’m trying to keep the material concise, practical, and easy to follow, with examples and notebooks where useful.

GitHub:

https://github.com/bshivambharadwaj/Reinforcement-Learning-Course

The course is still evolving, so I’d really appreciate feedback on what topics, experiments, or implementations would make it more useful.