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keywords: Julia
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# Learn You Some Reinforcement Learning
Here I will list everything that is helpful with a short note while implementing the reinforcement learning package [Ju.jl](https://github.com/Ju-jl/Ju.jl) in Julia.
## Slides
- [An Introduction to Reinforcement Learning](./slides/MSRA_RL_Essentials.pdf)
A concise introduction to RL by [Tao Qin](http://research.microsoft.com/users/taoqin/) from MSRA.
## Papers
- [An Introduction to Deep Reinforcement Learning.pdf](./papers/An_Introduction_to_Deep_Reinforcement_Learning.pdf)
A very comprehensive introduction. The most useful chapter to me is **Benchmarking Deep RL**.
- [Revisiting the Arcade Learning Environment.pdf](./papers/Revisiting_the_Arcade_Learning_Environment.pdf)
Because we will do a lot of experiments in the Atari environment, it's better to read this paper first to avoid some potential pitfalls.
- [Human-level control through deep reinforcement learning](./papers/DQNNaturePaper.pdf)
- [Playing Atari with Deep Reinforcement Learning](./papers/Playing_Atari_with_Deep_Reinforcement_Learning.pdf)
- [Deep Reinforcement Learning with Double Q-learning](./papers/Deep_Reinforcement_Learning_with_Double_Q-learning.pdf)
- [Prioritized Experience Replay](./papers/Prioritized_Experience_Replay.pdf)
- [Distributed Prioritized Experience Replay](./papers/Distributed_Prioritized_Experience_Replay.pdf)
## Codes
- [RL-Adventure](https://github.com/higgsfield/RL-Adventure)
A good start point. Although some implementations are not that efficient, this repo provides many code snippets that are easy to understand.
## Books
- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html)
It is said that everyone needs to take a look at this book before digging into reinforcement learning. And I have reproduced the figures with [Ju.jl](https://github.com/Ju-jl/Ju.jl) at [ReinforcementLearningAnIntroduction.jl](https://github.com/Ju-jl/ReinforcementLearningAnIntroduction.jl)
- [Dynamic Programming and Optimal Control](http://www.athenasc.com/dpbook.html)
I only finished the first volume. The latest edition has covered a lot of recent research results. It's a good supplementary of [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html).