A course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA(russian) and maintained to be friendly to online students (both english and russian).
- 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!
- Lecture slides are here.
- Telegram chat room for YSDA & HSE students is here
- Grading rules for YSDA & HSE students is here
- Online student survival guide
- Installing the libraries - guide and issues thread
- Magical button that launches you into course environment:
- Anonymous feedback form for everything that didn't go through e-mail.
- About the course
- A large list of RL materials - awesome rl
- RL reading group
The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.
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week1 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. Deadline: 2018.01.28 23:59
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week2 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. Deadline: 2018.02.11 23:59
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week3 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. Deadline: 2018.02.15 23:59
Course materials and teaching by
- Fedor Ratnikov - lectures, seminars, hw checkups
- Oleg Vasilev - seminars, hw checkups, technical support
- Pavel Shvechikov - lectures, seminars, hw checkups, reading group
- Alexander Fritsler - lectures, seminars, hw checkups
- Using pictures from Berkeley AI course
- Massively refering to CS294
- Sevaral tensorflow assignments by Scitator
- A lot of fixes from arogozhnikov
- Other awesome people: see github contributors