__Our slides:__ __[inverse/imitation rl](https://yadi.sk/i/ngB_BcNx3UggK6);__ __[multi-agent 101](https://yadi.sk/i/XrFgmdCy3Vtd4k);__ __[multi-agent 102](https://docs.google.com/presentation/d/1AiSZnWGHWU_34QZ0fqdCGIXWUX_bC-0zz3kc3VVcPCA/edit?usp=sharing);__ __[hierarchical rl](https://yadi.sk/i/LkNiKxMz3Vtcr3)__


In this week you can find several sections covering advanced topics in RL, along with less advanced topics that we couldn't squeeze into the main track

## Other
* Learning by imitation - [video](https://www.youtube.com/watch?v=kl_G95uKTHw), [assignment](http://rll.berkeley.edu/deeprlcourse/docs/hw1.pdf)(berkeley cs294)
* Inverse reinforcement learning 
  * Lecture by Chelsea Finn - [video](https://www.youtube.com/watch?v=d9DlQSJQAoI)
  * Udacity videos - [video](https://www.youtube.com/watch?v=h7uGyBcIeII)
* Distributional RL - [video](https://www.youtube.com/watch?v=bsuvM1jO-4w)
* Learning World Models for RL - [blog](https://worldmodels.github.io/)
* Knowledge transfer in RL - [video](https://www.youtube.com/watch?v=Hx4XpVdJOI0)(berkeley cs294)
* Hierarchical reinforcemnt learning 
  * Cool article ( Fe__U__dal networks ) - [arxiv](https://arxiv.org/abs/1703.01161)
  * Short lecture by Roy Fox - [video](https://www.youtube.com/watch?v=x_QjJry0hTc)
* Multi-Agent reinforcement learning
  * Lecture by Thoro Graepel - role of HRL in AI research - [video](https://www.youtube.com/watch?v=CvL-KV3IBcM)
  * Lecture by Balaraman Ravindran - [video](https://www.youtube.com/watch?v=K5MlmO0UJtI)

## A list of lists
* [awesome_rl](https://github.com/aikorea/awesome-rl/) - a curated list of resources dedicated to reinforcement learning.
* [junhyukoh's list](https://github.com/junhyukoh/deep-reinforcement-learning-papers)
* [muupan's list](https://github.com/muupan/deep-reinforcement-learning-papers)
* Courses:
 * [CS294: deep reinforcement learning](http://rll.berkeley.edu/deeprlcourse/)
 * [Silver's RL course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
 * [Sutton's book, 2nd edition](http://incompleteideas.net/sutton/book/the-book-2nd.html)
* [Implementations of many basic RL algorithms (raw and/or tensorflow)](https://github.com/dennybritz/reinforcement-learning)
* Reddit: [General ML](https://www.reddit.com/r/MachineLearning/), [RL](https://www.reddit.com/r/reinforcementlearning/), [CS294](https://www.reddit.com/r/berkeleydeeprlcourse/)
* [This great link you could have contributed]

