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# Reinforcement Learning Resources

Stable-Baselines3 assumes that you already understand the basic concepts of Reinforcement Learning (RL).

However, if you want to learn about RL, there are several good resources to get started:

- [OpenAI Spinning Up](https://spinningup.openai.com/en/latest/)
- [The Deep Reinforcement Learning Course](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
- [David Silver's course](https://davidstarsilver.wordpress.com/teaching/)
- [RL102: From Tabular Q-Learning to Deep Q-Learning (DQN)](https://araffin.github.io/post/rl102/)
- [RL103: From Deep Q-Learning (DQN) to Soft Actor-Critic (SAC) and Beyond](https://araffin.github.io/post/rl103/)
- [Lilian Weng's blog](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html)
- [Berkeley's Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)
- [Berkeley's Deep Reinforcement Learning course](https://rail.eecs.berkeley.edu/deeprlcourse/)
- [Decisions & Dragons - FAQ for RL foundations](https://www.decisionsanddragons.com)
- [More resources](https://github.com/dennybritz/reinforcement-learning)