casssini/practical_rl

A course in reinforcement learning in the wild

Practical_RL

An open course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian).

Note: this branch is an on-campus version of the for spring 2019 YSDA and HSE students. For full course materials, switch to the master branch.

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 it!

Course info

• Chat room for YSDA & HSE students is here

• Grading rules for YSDA & HSE students is here

• FAQ: About the course, Technical issues thread, Lecture Slides, Online Student Survival Guide

• Anonymous feedback form.

• Virtual course environment:

  • Installing dependencies on your local machine (recommended).
  • google colab - set open -> github -> yandexdataschool/pracical_rl -> {branch name} and select any notebook you want.
  • Alternatives: and Azure Notebooks.

Additional materials

• 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 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 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 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.

• week04 Approximate (deep) RL

• week05 Exploration

• week06 Policy Gradient methods

• week07 Applications I

• week{++i} Partially Observed MDP

• week{++i} Advanced policy-based methods

• week{++i} Applications II

• week{++i} Distributional reinforcement learning

• week{++i} Inverse RL and Imitation Learning

Course staff

Course materials and teaching by: [unordered]

• Pavel Shvechikov - lectures, seminars, hw checkups, reading group
• Nikita Putintsev - seminars, hw checkups, organizing our hot mess
• Alexander Fritsler - lectures, seminars, hw checkups
• Oleg Vasilev - seminars, hw checkups, technical support
• Dmitry Nikulin - tons of fixes, far and wide
• Mikhail Konobeev - seminars, hw checkups
• Ivan Kharitonov - seminars, hw checkups
• Ravil Khisamov - seminars, hw checkups
• Fedor Ratnikov - admin stuff

Contributions

• Using pictures from Berkeley AI course
• Massively refering to CS294
• Several tensorflow assignments by Scitator
• A lot of fixes from arogozhnikov
• Other awesome people: see github contributors
• Alexey Umnov helped us a lot during spring2018