#### __Lecture slides__ - [here](https://yadi.sk/i/9bPN0pxsR1-zIA)
### Materials
* Russian materials:
   - Lecture - [video](https://yadi.sk/i/jcQ1Bg8n3SrhuQ)
   - Seminars
      - Q-learning seminar - [video](https://yadi.sk/i/dQmolwOy3EtGNK) (older track - [assignment](./crawler_and_pacman/seminar_py2))
      - SARSA & stuff - [video](https://yadi.sk/i/XbqNQmjm3ExNsq)
* English materials:
   - Lecture by David Silver (english) - [video part I](https://www.youtube.com/watch?v=PnHCvfgC_ZA), [video part II](https://www.youtube.com/watch?v=0g4j2k_Ggc4&t=43s)
   - Alternative lecture by Pieter Abbeel (english) - [video](https://www.youtube.com/watch?v=ifma8G7LegE)
   - Alternative lecture by John Schulmann (english) - [video](https://www.youtube.com/watch?v=IL3gVyJMmhg)
   - Blog post on q-learning Vs SARSA - [url](https://studywolf.wordpress.com/2013/07/01/reinforcement-learning-sarsa-vs-q-learning/)

### More materials
* N-step temporal difference from Sutton's book - [suttonbook](http://incompleteideas.net/book/RLbook2018.pdf) __chapter 7__
* Eligibility traces from Sutton's book - [suttonbook](http://incompleteideas.net/book/RLbook2018.pdf) __chapter 12__
* Blog post on eligibility traces - [url](http://pierrelucbacon.com/traces/)

### Assignments

Just as usual, start with `homework.ipynb` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week03_model_free/homework.ipynb) For seminar, implement q-learning agent and test it on Taxi and CartPole with binarizer. And then, implement EV-SARSA agent, experience replay + bonus tasks for homework.
