* [Slides](https://yadi.sk/d/GG-GvN-13UhzFw)

* Video lecture by D. Silver - https://www.youtube.com/watch?v=KHZVXao4qXs

* Our [lecture](https://yadi.sk/i/I3M09HKQ3GKBiP), [seminar](https://yadi.sk/i/8f9NX_E73GKBkT)

* Alternative lecture by J. Schulman part 1 - https://www.youtube.com/watch?v=BB-BhTn6DCM

* Alternative lecture by J. Schulman part 2 - https://www.youtube.com/watch?v=Wnl-Qh2UHGg

* A full-term course on reinforcement learning - [practical_rl](https://github.com/yandexdataschool/practical_rl)

* Actually proving the policy gradient for discounted rewards - [article](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)

* On variance of policy gradient and optimal baselines: [article](https://papers.nips.cc/paper/4264-analysis-and-improvement-of-policy-gradient-estimation.pdf), another [article](https://arxiv.org/pdf/1301.2315.pdf)

* Generalized Advantage Estimation - a way you can speed up training for homework_*.ipynb - [article](https://arxiv.org/abs/1506.02438)

* Generalizing log-derivative trick - [url](http://blog.shakirm.com/2015/11/machine-learning-trick-of-the-day-5-log-derivative-trick/)

* Combining policy gradient and q-learning - [arxiv](https://arxiv.org/abs/1611.01626)

* Bayesian perspective on why reparameterization & logderivative tricks matter (Vetrov's take) - [pdf](https://www.sdsj.ru/slides/Vetrov.pdf)

* Adversarial review of policy gradient - [blog](http://www.argmin.net/2018/02/20/reinforce/)