https://github.com/sshkhr/practical_rl

My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow
https://github.com/sshkhr/practical_rl

bandit-algorithms deep-reinforcement-learning evolutionary-algorithms markov-decision-processes monte-carlo-sampling policy-gradient pytorch reinforcement-learning td-learning tensorflow

Last synced: 8 months ago
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My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow

• Host: GitHub
• URL: https://github.com/sshkhr/practical_rl
• Owner: sshkhr
• License: unlicense
• Created: 2018-05-10T15:56:20.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2021-12-22T20:23:43.000Z (almost 4 years ago)
• Last Synced: 2025-03-25T10:45:09.853Z (9 months ago)
• Topics: bandit-algorithms, deep-reinforcement-learning, evolutionary-algorithms, markov-decision-processes, monte-carlo-sampling, policy-gradient, pytorch, reinforcement-learning, td-learning, tensorflow
• Language: Jupyter Notebook
• Homepage: https://github.com/yandexdataschool/Practical_RL
• Size: 9.91 MB
• Stars: 54
• Watchers: 4
• Forks: 25
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# Practical_RL

A course on reinforcement learning in the wild.

Taught on-campus at [HSE](https://cs.hse.ru) and [YSDA](https://yandexdataschool.com/)  and maintained to be friendly to online students (both english and russian).

#### Manifesto:

* __Optimize for the curious.__ For all the materials that aren’t covered in detail there are links to more information and related materials (D.Silver/Sutton/blogs/whatever). Assignments will have bonus sections if you want to dig deeper.

* __Practicality first.__ Everything essential to solving reinforcement learning problems is worth mentioning. We won't shun away from covering tricks and heuristics. For every major idea there should be a lab that makes you to “feel” it on a practical problem.

* __Git-course.__ Know a way to make the course better? Noticed a typo in a formula? Found a useful link? Made the code more readable? Made a version for alternative framework? You're awesome! [Pull-request](https://help.github.com/articles/about-pull-requests/) it!

# Course info

* Lecture slides are [here](https://yadi.sk/d/loPpY45J3EAYfU).

* Telegram chat room for YSDA & HSE students is [here](https://t.me/rlspring18)

* Grading rules for YSDA & HSE students is [here](https://github.com/yandexdataschool/Practical_RL/wiki/Homeworks-and-grading)

* Online student __[survival guide](https://github.com/yandexdataschool/Practical_RL/wiki/Online-student's-survival-guide)__

* Installing the libraries - [guide and issues thread](https://github.com/yandexdataschool/Practical_RL/issues/1)

* Magical button that launches you into course environment: 

    * [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/yandexdataschool/Practical_RL/master) - comes will all libraries pre-installed. May be down time to time.

    * If it's down, try [__google colab__](https://colab.research.google.com/) or [__azure notebooks__](http://notebooks.azure.com/). Those last longer, but they will require you to run installer commands (see ./Dockerfile).

* Anonymous [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSdurWw97Sm9xCyYwC8g3iB5EibITnoPJW2IkOVQYE_kcXPh6Q/viewform) for everything that didn't go through e-mail.

* [About the course](https://github.com/yandexdataschool/Practical_RL/wiki/Practical-RL)

# Additional materials

* A large list of RL materials - [awesome rl](https://github.com/aikorea/awesome-rl)

* [RL reading group](https://github.com/yandexdataschool/Practical_RL/wiki/RL-reading-group)

# Syllabus

The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.

* [__week1__](https://github.com/yandexdataschool/Practical_RL/tree/master/week1_intro) RL as blackbox optimization

  * Lecture: RL problems around us. Decision processes. Stochastic optimization, Crossentropy method. Parameter space search vs action space search.

  * Seminar: Welcome into openai gym. Tabular CEM for Taxi-v0, deep CEM for box2d environments.

  * Homework description - see week1/README.md. 

  * ** YSDA Deadline: 2018.02.26 23.59**

  * ** HSE Deadline: 2018.01.28 23:59**

  

* [__week2__](https://github.com/yandexdataschool/Practical_RL/tree/master/week2_value_based) Value-based methods

  * Lecture: Discounted reward MDP. Value-based approach. Value iteration. Policy iteration. Discounted reward fails.

  * Seminar: Value iteration.  

  * Homework description - see week2/README.md. 

  * ** HSE Deadline: 2018.02.11 23:59**

  * ** YSDA Deadline: part1 2018.03.05 23.59, part2 2018.03.12 23.59**

  

* [__week3__](https://github.com/yandexdataschool/Practical_RL/tree/master/week3_model_free) Model-free reinforcement learning

  * Lecture: Q-learning. SARSA. Off-policy Vs on-policy algorithms. N-step algorithms. TD(Lambda).

  * Seminar: Qlearning Vs SARSA Vs Expected Value SARSA

  * Homework description - see week3/README.md. 

  * **HSE Deadline: 2018.02.15 23:59**

  * ** YSDA Deadline: 2018.03.12 23.59**

     

* [__week4_recap__](https://github.com/yandexdataschool/Practical_RL/tree/master/week4_%5Brecap%5D_deep_learning) - deep learning recap 

  * Lecture: Deep learning 101

  * Seminar: Simple image classification with convnets

* [__week4__](https://github.com/yandexdataschool/Practical_RL/tree/master/week4_approx_rl) Approximate reinforcement learning

  * Lecture: Infinite/continuous state space. Value function approximation. Convergence conditions. Multiple agents trick; experience replay, target networks, double/dueling/bootstrap DQN, etc.

  * Seminar:  Approximate Q-learning with experience replay. (CartPole, Atari)

  * **HSE Deadline: 2018.03.04 23:30**

  * ** YSDA Deadline: 2018.03.20 23.30**

* [__week5__](https://github.com/yandexdataschool/Practical_RL/tree/master/week5_explore) Exploration in reinforcement learning

  * Lecture: Contextual bandits. Thompson Sampling, UCB, bayesian UCB. Exploration in model-based RL, MCTS. "Deep" heuristics for exploration.

  * Seminar: bayesian exploration for contextual bandits. UCB for MCTS.

  

  * ** YSDA Deadline: 2018.03.30 23.30**

* [__week6__](https://github.com/yandexdataschool/Practical_RL/tree/master/week6_policy_based) Policy gradient methods I

  * Lecture: Motivation for policy-based, policy gradient, logderivative trick, REINFORCE/crossentropy method, variance reduction(baseline), advantage actor-critic (incl. GAE)

  * Seminar: REINFORCE, advantage actor-critic

* [__week7_recap__](https://github.com/yandexdataschool/Practical_RL/tree/master/week7_%5Brecap%5D_rnn) Recurrent neural networks recap

  * Lecture: Problems with sequential data. Recurrent neural netowks. Backprop through time. Vanishing & exploding gradients. LSTM, GRU. Gradient clipping

  * Seminar: character-level RNN language model

* [__week7__](https://github.com/yandexdataschool/Practical_RL/tree/master/week7_pomdp) Partially observable MDPs

  * Lecture: POMDP intro. POMDP learning (agents with memory). POMDP planning (POMCP, etc)

  * Seminar: Deep kung-fu & doom with recurrent A3C and DRQN

    

* [__week8__](https://github.com/yandexdataschool/Practical_RL/tree/master/week8_scst) Applications II

  * Lecture: Reinforcement Learning as a general way to optimize non-differentiable loss. G2P, machine translation, conversation models, image captioning, discrete GANs. Self-critical sequence training.

  * Seminar: Simple neural machine translation with self-critical sequence training

* [__week9__](https://github.com/yandexdataschool/Practical_RL/tree/master/week9_policy_II) Policy gradient methods II

  * Lecture: Trust region policy optimization. NPO/PPO. Deterministic policy gradient. DDPG. Bonus: DPG for discrete action spaces.

  * Seminar: Approximate TRPO for simple robotic tasks.

* [Some after-course bonus materials](https://github.com/yandexdataschool/Practical_RL/tree/master/yet_another_week)

  

# Course staff

Course materials and teaching by: _[unordered]_

- [Pavel Shvechikov](https://github.com/bestxolodec) - lectures, seminars, hw checkups, reading group

- [Oleg Vasilev](https://github.com/Omrigan) - seminars, hw checkups, technical support

- [Alexander Fritsler](https://github.com/Fritz449) - lectures, seminars, hw checkups

- [Nikita Putintsev](https://github.com/qwasser) - seminars, hw checkups, organizing our hot mess

- [Fedor Ratnikov](https://github.com/justheuristic/) - lectures, seminars, hw checkups

- [Alexey Umnov](https://github.com/alexeyum) - seminars, hw checkups

# Contributions

* Using pictures from [Berkeley AI course](http://ai.berkeley.edu/home.html)

* Massively refering to [CS294](http://rll.berkeley.edu/deeprlcourse/)

* Sevaral tensorflow assignments by [Scitator](https://github.com/Scitator)

* A lot of fixes from [arogozhnikov](https://github.com/arogozhnikov)

* Other awesome people: see github contributors