---
title: "Reinforcement learning resources"
description: "Curated RL courses, lectures, and papers — from bandits and MDPs to policy gradients and RLHF."
canonical_url: "https://fanout.sh/ai/reinforcement-learning"
md_url: "https://fanout.sh/ai/reinforcement-learning.md"
access: "public"
---

# Reinforcement learning resources

Curated RL courses, lectures, and papers — from bandits and MDPs to policy gradients and RLHF.

## Public reinforcement learning resources

From policy gradients to deep Q-networks — books, courses, and frameworks for learning how agents learn from rewards.

## Start here

### [Reinforcement Learning: An Introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf)

- Author: Sutton & Barto
- Type: book

### [Introduction to Reinforcement Learning 2015](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)

- Author: DeepMind x UCL
- Type: course

### [Spinning Up in Deep RL](https://github.com/openai/spinningup)

- Author: OpenAI
- Type: repo

### [Algorithms for Reinforcement Learning](https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs-lecture.pdf)

- Author: Csaba Szepesvári
- Type: book

### [CS 294: Deep Reinforcement Learning](https://rll.berkeley.edu/deeprlcoursesp17/)

- Author: UC Berkeley
- Type: course

---
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