> For the complete documentation index, see [llms.txt](https://slm-lab.gitbook.io/slm-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slm-lab.gitbook.io/slm-lab/v4.2.0/resources/untitled.md).

# Deep RL Resources

## :pushpin: Master List of Deep RL Resources

{% embed url="<https://github.com/kengz/awesome-deep-rl>" %}

## :book: Books

* Graesser and Keng, [Foundations of Deep Reinforcement Learning](https://www.amazon.com/dp/0135172381)
* Sutton and Barto, [Reinforcement Learning: An Introduction](https://www.amazon.com/dp/0262039249)
* Francois-Lavet et. al., [An Introduction to Deep Reinforcement Learning](https://www.amazon.com/dp/1680835386)

## :classical\_building: Introductory Tutorials / Courses

* [Andrew Karpathy Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)
* [Arthur Juliani Simple Reinforcement Learning in Tensorflow Series](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)
* [David Silver UCL Course on RL 2015](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* [Deep RL Bootcamp 2017](https://sites.google.com/view/deep-rl-bootcamp/lectures)
* [DeepMind UCL Deep RL Course 2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)
* [dennybritz/reinforcement-learning](https://github.com/dennybritz/reinforcement-learning)
* [higgsfield/RL-Adventure-2](https://github.com/higgsfield/RL-Adventure-2)
* [higgsfield/RL-Adventure](https://github.com/higgsfield/RL-Adventure)
* [MorvanZhou/Reinforcement Learning Methods and Tutorials](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow)
* [OpenAI Spinning Up](https://github.com/openai/spinningup)
* [Sergey Levine CS294 Deep Reinforcement Learning Fall 2017](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/index.html)

## :microscope: Papers

* [DQN](https://arxiv.org/abs/1312.5602)
* [Double DQN](https://arxiv.org/abs/1509.06461) (DDQN)
* [Dueling DQN](https://arxiv.org/abs/1511.06581)
* [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952) (PER)
* [Combined Experience Replay](https://arxiv.org/abs/1712.01275) (CER)
* [Hindsight Experience Replay](https://arxiv.org/abs/1707.01495) (HER)
* [QT-Opt](https://arxiv.org/abs/1806.10293)
* [Asynchronous Advantage Actor Critic](https://arxiv.org/abs/1602.01783) (A3C)
* [Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438) (GAE)
* [Proximal Policy Optimization](https://arxiv.org/abs/1707.06347) (PPO)
* [Self Imitation Learning](https://arxiv.org/abs/1806.05635) (SIL)
* [Soft Actor-Critic](https://arxiv.org/abs/1812.05905) (SAC)


---

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