# Key Papers

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Deep Learning Theory: <https://stats385.github.io/readings>, FAST.AI (practical)

### Reinforcement Basics:

* Richard Sutton Book
* Emma and stanford COurse:&#x20;
* David Silver Course: [course](https://www.youtube.com/watch?v=2pWv7GOvuf0\&t=1376s)

<br>

### Classic Papers:

* MDPs: [Bellman 1957](http://www.jstor.org/stable/24900506?casa_token=Y1sRbXJZdXsAAAAA:QTg2yoUhh7E3fh5y-Q1jszhP1b1s2TwcJd-uQiknz1V40yo-PfrpddvtkSmiNRL_bhA5WEq29dVnocSJEJTiLQw2xM_Sjz0XGjuVAeE-RFz8N0FnBzjl)
* TD-Learning: [Sutton 1988](https://link.springer.com/content/pdf/10.1007/BF00115009.pdf)
* Q-Learning: [Watkins and Dayan 1992](https://link.springer.com/content/pdf/10.1007/BF00992698.pdf)
* REINFORCE: [Williams 1992](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.129.8871\&rep=rep1\&type=pdf)
* Prioritized Sweeping: [Moore and Atkeson 1993](https://link.springer.com/content/pdf/10.1007/BF00993104.pdf)
* Markov Games and RL: [Littman 1994](http://students.cs.byu.edu/~cs670ta/Fall2009/MinimaxQLearning.pdf)
* Value function approximation: [Boyan and Moore 1995](http://papers.nips.cc/paper/1018-generalization-in-reinforcement-learning-safely-approximating-the-value-function.pdf)
* Complexity of MDPs: [Littman, Dean, Kaelbling 1995](https://arxiv.org/pdf/1302.4971.pdf)
* RL for Backgammon: [Tesauro 1995](http://enzodesiage.com/wp-content/uploads/2017/08/tesauro-tdgammon-1995.pdf)
* POMDPs: [Kaelbling, Littman, Cassandra 1998](http://www.ai.mit.edu/courses/6.825/pdf/pomdp.pdf)
* Reward Shaping: [Ng, Harada, and Russell 1999](http://robotics.stanford.edu/~ang/papers/shaping-icml99.pdf)
* Policy Gradients: [Sutton, McAllaster, Singh, Mansour 2000](http://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)
* Options/Temporal Abstractions: [Sutton, Precup, Singh](https://s3.amazonaws.com/academia.edu.documents/42892118/Between_MDPs_and_Semi-MDPs_A_Framework_f20160221-9287-1r7rppa.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A\&Expires=1526562216\&Signature=uYjQnexJbH7ukQQCUyGmz5bL9Ao%3D\&response-content-disposition=inline%3B%20filename%3DBetween_MDPs_and_semi-MDPs_A_framework_f.pdf)
* Hierarchical RL: [Dietterich 2000](http://aima.eecs.berkeley.edu/~russell/classes/cs294/s11/readings/Dietterich:2000.pdf)
* Model-based RL with guarantees #1: [Kearns and Singh 2002](https://link.springer.com/content/pdf/10.1023/A:1017984413808.pdf)
* Model-based RL with guarantees #2: [Braffman and Tennenholtz 2002](http://www.jmlr.org/papers/volume3/brafman02a/brafman02a.pdf)
* Inverse RL: [Abbeil and Ng 2004](http://people.eecs.berkeley.edu/~russell/classes/cs294/s11/readings/Abbeel+Ng:2004.pdf)

### Deep RL:&#x20;

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Deep RL David Silver Tutorial: watch NIPS keynote and [DRL tutorial](https://youtu.be/M5a6HasTHs4) and Survey Papers: <br>

1. [DEEP REINFORCEMENT LEARNING: AN OVERVIEW](https://arxiv.org/pdf/1701.07274.pdf,),&#x20;
2. &#x20;<https://arxiv.org/pdf/1708.05866.pdf>
3. [A long peek into RL](https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html)
4. [Principles of deep RL: ](http://www.deeplearningindaba.com/uploads/1/0/2/6/102657286/principles_of_deep_rl.pdf)
5. Critique: <https://www.alexirpan.com/2018/02/14/rl-hard.html>
