# Practical_RL

**Repository Path**: thb1314/Practical_RL

## Basic Information

- **Project Name**: Practical_RL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Unlicense
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-13
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# Practical_RL [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/master)
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
* __Chat room__ for YSDA & HSE students is [here](https://t.me/joinchat/CDFcMVcoAQvEiI9WAo1pEQ)
* __Grading__ rules for YSDA & HSE students is [here](https://github.com/yandexdataschool/Practical_RL/wiki/Homeworks-and-grading)

* __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: 
    * [Installing dependencies](https://github.com/yandexdataschool/Practical_RL/issues/1) on your local machine (recommended).
    * [__google colab__](https://colab.research.google.com/) - set open -> github -> yandexdataschool/pracical_rl -> {branch name} and select any notebook you want.
    * Alternatives: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/master) and [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 netowks. 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/bestxolodec) - 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
- [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