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https://github.com/yandexdataschool/practical_rl

A course in reinforcement learning in the wild
https://github.com/yandexdataschool/practical_rl

course-materials deep-learning deep-reinforcement-learning git-course hacktoberfest keras mooc pytorch pytorch-tutorials reinforcement-learning tensorflow

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A course in reinforcement learning in the wild

Host: GitHub
URL: https://github.com/yandexdataschool/practical_rl
Owner: yandexdataschool
License: unlicense
Created: 2017-01-23T16:18:00.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2024-12-23T10:32:04.000Z (29 days ago)
Last Synced: 2025-01-13T16:55:02.664Z (8 days ago)
Topics: course-materials, deep-learning, deep-reinforcement-learning, git-course, hacktoberfest, keras, mooc, pytorch, pytorch-tutorials, reinforcement-learning, tensorflow
Language: Jupyter Notebook
Homepage:
Size: 45.7 MB
Stars: 5,970
Watchers: 210
Forks: 1,696
Open Issues: 39
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Awesome Lists containing this project

README

        # Practical_RL

An open 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!

[![Github contributors](https://img.shields.io/github/contributors/yandexdataschool/Practical_RL.svg?logo=github&logoColor=white)](https://github.com/yandexdataschool/Practical_RL/graphs/contributors)

# Course info

* __FAQ:__ [About the course](https://github.com/yandexdataschool/Practical_RL/wiki/Practical-RL), [Technical issues thread](https://github.com/yandexdataschool/Practical_RL/issues/1), [Lecture Slides](https://yadi.sk/d/loPpY45J3EAYfU), [Online Student Survival Guide](https://github.com/yandexdataschool/Practical_RL/wiki/Online-student's-survival-guide)

* Anonymous [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSdurWw97Sm9xCyYwC8g3iB5EibITnoPJW2IkOVQYE_kcXPh6Q/viewform).

* Virtual course environment: 

    * [__Google Colab__](https://colab.research.google.com/) - set open -> github -> yandexdataschool/pracical_rl -> {branch name} and select any notebook you want.

    * [Installing dependencies](https://github.com/yandexdataschool/Practical_RL/issues/1) on your local machine (recommended).

    * Alternative: [Azure Notebooks](https://notebooks.azure.com/).

# Additional materials

* [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.

* [__week01_intro__](./week01_intro) Introduction

  * 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. 

* [__week02_value_based__](./week02_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. 

  

* [__week03_model_free__](./week03_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. 

* [__recap_deep_learning__](./week04_\[recap\]_deep_learning) - deep learning recap 

  * Lecture: Deep learning 101

  * Seminar: Intro to pytorch/tensorflow, simple image classification with convnets

* [__week04_approx_rl__](./week04_approx_rl) Approximate (deep) RL

  * 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)

  

* [__week05_explore__](./week05_explore) Exploration

  * 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.

* [__week06_policy_based__](./week06_policy_based) Policy Gradient methods

  * 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

* [__week07_seq2seq__](./week07_seq2seq) Reinforcement Learning for Sequence Models

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

  * Seminar: character-level RNN language model

* [__week08_pomdp__](./week08_pomdp) Partially Observed MDP

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

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

  

* [__week09_policy_II__](./week09_policy_II) Advanced policy-based methods

  * Lecture: Trust region policy optimization. NPO/PPO. Deterministic policy gradient. DDPG

  * Seminar: Approximate TRPO for simple robot control.

* [__week10_planning__](./week10_planning) Model-based RL & Co

  * Lecture: Model-Based RL, Planning in General, Imitation Learning and Inverse Reinforcement Learning

  * Seminar: MCTS for toy tasks

* [__yet_another_week__](./yet_another_week) Inverse RL and Imitation Learning

  * All that cool RL stuff that you won't learn from this course :)

# Course staff

Course materials and teaching by: _[unordered]_

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

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

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

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

- [Dmitry Nikulin](https://github.com/pastafarianist) - tons of fixes, far and wide

- [Mikhail Konobeev](https://github.com/MichaelKonobeev) - seminars, hw checkups

- [Ivan Kharitonov](https://github.com/neer201) - seminars, hw checkups

- [Ravil Khisamov](https://github.com/zshrav) - seminars, hw checkups

- [Anna Klepova](https://github.com/q0o0p) - hw checkups

- [Fedor Ratnikov](https://github.com/justheuristic) - admin stuff

# Contributions

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

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

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

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

* Other awesome people: see github [contributors](https://github.com/yandexdataschool/Practical_RL/graphs/contributors)

* [Alexey Umnov](https://github.com/alexeyum) helped us a lot during spring2018