# Reinforcement Learning

RL for Games :-

* [The FASTEST introduction to Reinforcement Learning on the internet](https://www.youtube.com/watch?v=VnpRp7ZglfA)
* <http://incompleteideas.net/book/RLbook2020.pdf>
* [Training AI to Play Fortnite (Reinforcement Learning + Computer Vision)](https://www.youtube.com/watch?v=iFAe7x2Nos8)
* [Python Reinforcement Learning using Gymnasium – Full Course](https://www.youtube.com/watch?v=vufTSJbzKGU)
* [Easiest Way to Train AI to Play Atari Games with Reinforcement Learning | RL Baselines3 Zoo Tutorial](https://www.youtube.com/watch?v=aQsaH7Tzvp0)
* [Reinforcement Learning for Gaming | Full Python Course in 9 Hours](https://www.youtube.com/watch?v=dWmJ5CXSKdw)
* [Game Automation with YOLOv8: Python Bot Tutorial](https://www.youtube.com/watch?v=PDzifxuEric)
* [Training AI to Play Pokemon with Reinforcement Learning](https://www.youtube.com/watch?v=DcYLT37ImBY)
* [Training an unbeatable AI in Trackmania](https://www.youtube.com/watch?v=Dw3BZ6O_8LY)
* [Intro to Game AI and Reinforcement Learning](https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://59r.gitbook.io/ml-university/reinforcement-learning.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
