└── README.md


/README.md:
--------------------------------------------------------------------------------
 1 | # Learning Optimal Control
 2 | Resources for learning control, optimal control, robotics, reinforcement learning
 3 | 
 4 | This list is everything but complete ...
 5 | 
 6 | ## Lectures/Online courses
 7 | 
 8 | ### Underactuated Robotics MIT 6.832
 9 | This lecture held by [Russ Tedrake](https://groups.csail.mit.edu/locomotion/russt.html) is an excellent resource covering very important concepts of optimal control.
10 | 
11 | #### Video
12 | - [2015](https://www.youtube.com/playlist?list=PLnWkMhyDLp1CGlkfJqFBLmTs5Nf2RWWGe)
13 | - [2009](https://www.youtube.com/playlist?list=PL58F1D0056F04CF8C)
14 | #### Online course
15 | [edX Underactuated Robotics](https://www.edx.org/course/underactuated-robotics-mitx-6-832x-0)
16 | #### Material
17 | [Working notes for lecture and online course](http://underactuated.csail.mit.edu/underactuated.html)
18 | 
19 | ---
20 | 
21 | ### UCL Course on RL
22 | 
23 | A great introduction to Reinforcement Learning given by David Silver, one of the brains behind DeepMind.
24 | 
25 | #### Material 
26 | [Lecture slides, exam with answers](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
27 | 
28 | #### Video
29 | - [Lecture 1-10](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)
30 | 
31 | ---
32 | 
33 | ### Deep Reinforcement Learning CS294-112
34 | 
35 | #### Material
36 | [Lecture slides, homeworks](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
37 | 
38 | 
39 | #### Video
40 | [Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37)
41 | 
42 | ---
43 | 
44 | ### Deep RL Bootcamp
45 | 
46 | #### Material & Video
47 | [Slides, videos](https://sites.google.com/view/deep-rl-bootcamp/lectures)
48 | 
49 | ---
50 | 
51 | ### Intelligent control through learning and optimization AMATH / CSE 579
52 | 
53 | Besides the lecture slides, [Emo Todorov](http://homes.cs.washington.edu/~todorov/) is also providing a list of relevant papers and general purpose readings.
54 | 
55 | #### Material 
56 | [https://homes.cs.washington.edu/~todorov/courses/amath579/](https://homes.cs.washington.edu/~todorov/courses/amath579/)
57 | 
58 | ---
59 | 
60 | ### Reinforcement Learning by Georgia Tech
61 | 
62 | #### Online course
63 | [Udacity Reinforcement Learning by Georgia Tech](https://www.udacity.com/course/reinforcement-learning--ud600)
64 | 
65 | 
66 | ## Blogs
67 |  - [Studywolf](https://studywolf.wordpress.com/)
68 |  - [WildML](http://www.wildml.com/)
69 |  - [argmin blog](http://www.argmin.net) (especially [An Outsider's Tour of Reinforcement Learning](http://www.argmin.net/2018/06/25/outsider-rl/))
70 | 
71 | ## Articles 
72 | 
73 |  - [Reinforcement Learning Algorithms Quick Overview](https://medium.com/@jonathan_hui/rl-reinforcement-learning-algorithms-quick-overview-6bf69736694d)
74 | 
75 | ## Tutorials
76 | 
77 | ## Videos
78 | 
79 | [Deep Reinforcement Learning (David Silver, 2015)](http://videolectures.net/rldm2015_silver_reinforcement_learning/)
80 | 
81 | [ICML 2018: Tutorial Session: Optimization Perspectives on Learning to Control (Ben Recht)](https://www.youtube.com/watch?v=hYw_qhLUE0o)
82 | 
83 | ## Books 
84 | 
85 |  - [Dynamic Programming and Optimal Control Vol. 1 + 2](http://web.mit.edu/dimitrib/www/dpchapter.html)
86 |  - [Reinforcement Learning: An Introduction](https://mitpress.mit.edu/books/reinforcement-learning) ([PDF](http://incompleteideas.net/book/bookdraft2017nov5.pdf))
87 |  - [Neuro-Dynamic Programming](http://web.mit.edu/jnt/www/ndp.html)
88 |  - [Probabilistic Robotics](http://www.probabilistic-robotics.org/)
89 |  - [Springer Handbook of Robotics](https://www.springer.com/de/book/9783540303015)
90 |  - [Robotics - Modelling, Planning, Control](https://www.springer.com/de/book/9781846286414)
91 |  
92 | 


--------------------------------------------------------------------------------