## Materials
* [Slides](https://yadi.sk/i/keSzKSgA2oYuwQ)
* Video lecture by D. Silver - [video](https://www.youtube.com/watch?v=KHZVXao4qXs)
* Our [lecture](https://yadi.sk/i/yPIPkO_f3TPsNK), [seminar(pytorch)](https://yadi.sk/i/flW8ezGk3TPsQ5)
* Alternative lecture by J. Schulman part 1 - [video](https://www.youtube.com/watch?v=BB-BhTn6DCM)
* Alternative lecture by J. Schulman part 2 - [video](https://www.youtube.com/watch?v=Wnl-Qh2UHGg)
* Andrej Karpathy's [post](http://karpathy.github.io/2016/05/31/rl/) on policy gradients


## More materials
* 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)
* Learn Advatangeg Actor Critic with a [comic](https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752)


* 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)
* Variational perspective on reinforcement learning (from DeepBayes) - [pdf](http://incompleteideas.net/book/the-book-2nd.html)
* Adversarial review of policy gradient - [blog](http://www.argmin.net/2018/02/20/reinforce/)


Run seminar notebook in Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week06_policy_based/reinforce_pytorch.ipynb)

Run optional homework notebook in Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week06_policy_based/a2c-optional.ipynb)
