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).
- 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!
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Chat room for YSDA & HSE students is here
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Grading rules for YSDA & HSE students is here
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FAQ: About the course, Technical issues thread, Lecture Slides, Online Student Survival Guide
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Anonymous feedback form.
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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.
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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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.
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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.
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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.
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recap_deep_learning - deep learning recap
- Lecture: Deep learning 101
- Seminar: Intro to pytorch/tensorflow, simple image classification with convnets
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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)
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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.
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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
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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
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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
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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.
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week10_planning Model-based RL & Co
- Lecture: Model-Based RL, Planning in General, Imitation Learning and Inverse Reinforcement Learning
- Seminar: MCTS for toy tasks
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yet_another_week Inverse RL and Imitation Learning
- All that cool RL stuff that you won't learn from this course :)
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
- 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