Resources to get started with deep reinforcement learning Assume the learner is proficient with artificial neural networks, and has some background in reinforcement learning. What are some good resources (books/videos/papers/GitHub repo/etc.) to get started with deep reinforcement learning?
 A: A nice introduction deep reinforcement learning  by Lex Fridman (2019-01): https://youtu.be/zR11FLZ-O9M
2 complimentary, easy-to-read blog posts to get started on deep reinforcement learning: the first one focuses on policy gradients, the second one focuses on deep Q-learning.


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*Deep Reinforcement Learning: Pong from Pixels (mirror) by Andrej Karpathy (May 31, 2016).

*Demystifying Deep Reinforcement Learning (mirror) by  Tambet Matiise on Nervana (December 21, 2015)


Then, two more in-depth resources:


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*10 videos, ~90 minutes each: Advanced Topics  2015 (COMPM050/COMPGI13)
 (mirror) on Reinforcement Learning by David Silver (2015)

*455-page free  book: Reinforcement Learning: An Introduction (2nd Edition) by Richard S. Sutton and Andrew G. Barto (2016)


To start coding:


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*Learning Reinforcement Learning (with Code, Exercises and Solutions)
 (mirror) by Denny Britz (October 2, 2016)

*Minimal and Clean Reinforcement Learning Examples (2017)

*Using Keras and Deep Q-Network to Play FlappyBird (mirror, code) by Ben Lau (July 10, 2016) (the code is straightforward to run on Ubuntu)

*Using Keras and Deep Deterministic Policy Gradient to play TORCS (mirror, code) by Ben Lau (October 11, 2016) (note: installing the gym_torcs requirement to have the code run may take some time, and instructions are only given for Linux). 


More links:


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*A curated list of resources dedicated to reinforcement learning.
A: You can see many resources on github already including this recent list of Deep RL papers 
Also checkout some implementations such as Deep Q learning
And here is a nice video by David Silver at RLDM videolectures.net deep RL
