A course in reinforcement learning in the wild
Grading rules for YSDA & HSE students is here
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Virtual course environment:
The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.
week02valuebased Value-based methods
week03modelfree Model-free reinforcement learning
recapdeeplearning - deep learning recap
week04approxrl Approximate (deep) RL
week06policybased Policy Gradient methods
week07_seq2seq Reinforcement Learning for Sequence Models
week08_pomdp Partially Observed MDP
week09policyII Advanced policy-based methods
week10_planning Model-based RL & Co
yetanotherweek Inverse RL and Imitation Learning
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 - Anna Klepova - hw checkups - Fedor Ratnikov - admin stuff