[alkhatib]

I came across this a few days ago: For Reinforcement Learning specifically, the standard text is Reinforcement Learning: An Introduction[1]. Dave's UCL Course on RL[2] is great too (playlist of all lectures)[3].

Source: Julian Schrittwieser works on Deepmind at Google http://www.furidamu.org/

[1]http://incompleteideas.net/book/the-book-2nd.html

[2]http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

[3]https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r...

[MasterScrat]

Beyond those, my favorite resources are:

- "An Introduction to Deep Reinforcement Learning" by Vincent François-Lavet et al (https://arxiv.org/pdf/1811.12560.pdf)

- "A (Long) Peek into Reinforcement Learning" by Lilian Weng (https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-...)

- "Deep Reinforcement Learning: Pong from Pixels" from Andrej Karpathy (https://karpathy.github.io/2016/05/31/rl/)

Those are the basics. Some more resources listed on this post: https://news.ycombinator.com/item?id=18219620

[kejaed]

Thanks for this list. I’d like to get up to speed on RL and see how we can apply it for path planning and control of our gliding parachute UAVs.

[jcims]

Ooh! I want one of those for high altitude balloon payload recovery. Got any small ones or hobby grade projects doing this that you’re aware of?

[kejaed]

No hobby ones I know of, although I haven't looked into the hobby space too much. All I can say about smaller systems is give our BD guys a call... =D

[mark_l_watson]

thanks!!