/Practical_RL

A course in reinforcement learning in the wild

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Practical_RL Binder

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

Additional materials

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]

Contributions