A course on reinforcement learning in the wild. Taught on-campus in HSE and Yandex SDA (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 allows 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!

• HSE classes are on mondays at 18-10 in Room 505
• YSDA classes are on thursdays at 18-00 in "Princeton" classroom
• Lecture slides are here.
• Online student survival guide
• Installing the libraries - guide and issues thread
• Magical button that creates VM: (may be down time to time. If it won't load for 2-3 minutes, it's down)
• Telegram chat room (russian)
• Gitter chat room (english)
• How to submit homeworks[HSE and YSDA only]: anytask instructions and grading rules
• E-mail for everything else : [email protected] (please don't submit homeworks via e-mail)
• Anonymous feedback form for everything that didn't go through e-mail.
• About the course

• 25.03.16 - HSE important next monday lecture is postponed by 1 week due to HSE mid-term exams. Deadlines have been postponed accordingly.
• 25.03.16 - week5 you can submit any atari game you want.
• 16.03.16 - week4 homework max score threshold for LunarLander reduced to -100
• 16.03.16 - (hse) shifted deadline for week5
• 15.03.16 - (hse) added week6 assignment and deadline
• 10.03.17 - (ysda/hse students) important please consider Course Projects as an alternative way of completing the course.
• 8.03.17 - YSDA deadlines announced for weeks 3 and 3.5, sry for only doing this now.
• 01.03.17 - YSDA deadline on week2 homework moved to 08.03.17

• week0 Welcome to the MDP

• Lecture: RL problems around us. Markov decision process. Simple solutions through combinatoric optimization.

• Seminar: Frozenlake with genetic algorithms

• Homework description - week0/README.md

  • HSE Homework deadline: 23.59 1.02.17
  • YSDA Homework deadline: 23.59 19.02.17

• week1 Crossentropy method and monte-carlo algorithms

• Lecture: Crossentropy method in general and for RL. Extension to continuous state & action space. Limitations.

• Seminar: Tabular CEM for Taxi-v0, deep CEM for box2d environments.

• Homework description - week1/README.md

  • HSE homework deadline: 23.59 15.02.17
  • YSDA homework deadline: 23.59 26.02.17

• week2 Temporal Difference

• Lecture: Discounted reward MDP. Value iteration. Q-learning. Temporal difference Vs Monte-Carlo.

• Seminar: Tabular q-learning

• Homework description - week2/README.md

  • HSE homework deadline: 23.59 15.02.17
  • YSDA homework deadline: 23.59 8.03.17

• week3 Value-based algorithms

• Lecture: SARSA. Off-policy Vs on-policy algorithms. N-step algorithms. Eligibility traces.

• Seminar: Qlearning Vs SARSA Vs expected value sarsa in the wild

• Homework description - week3/README.md

  • HSE homework deadline 23.59 22.02.17
  • YSDA homework deadline: 23.59 14.03.17

• week3.5 Deep learning recap

• Lecture: deep learning, convolutional nets, batchnorm, dropout, data augmentation and all that stuff.

• Seminar: Theano/Lasagne on mnist, simple deep q-learning with CartPole (TF version contrib is welcome)

• Homework - convnets on MNIST or simple deep q-learning - week3.5/README.md

  • HSE homework deadline 23.59 1.03.17
  • YSDA homework deadline: 23.59 14.03.17 (5 pts)

• week4 Approximate reinforcement learning

• Lecture: Infinite/continuous state space. Value function approximation. Convergence conditions. Multiple agents trick.

• Seminar: Approximate Q-learning with experience replay. (CartPole, Acrobot, Doom)

• Homework - q-learning manually, experience replay - week4/README.md

  • HSE homework deadline 23.59 8.03.17
  • YSDA homework deadline 23.59 19.03.17

• week5 Deep reinforcement learning

• Lecture: Deep Q-learning/sarsa/whatever. Heuristics & motivation behind them: experience replay, target networks, double/dueling/bootstrap DQN, etc.

• Seminar: DQN on atari

• Homework - Breakout with DQN and advanced tricks - week5/README.md

  • HSE homework deadline 23.59 22.03.17
  • YSDA homework deadline 23.59 26.03.17

• week6 Policy gradient methods

• Lecture: Motivation for policy-based, policy gradient, logderivative trick, REINFORCE/crossentropy method, variance theorem(advantage), advantage actor-critic (incl.n-step advantage)

• Seminar: REINFORCE manually, advantage actor-critic for MountainCar - week6/README.md

  • HSE homework deadline 23.59 2.04.17
  • YSDA deadline 23.59 6.04.2017

• week6.5 RNN recap

• Lecture: recurrent neura networks for sequences. GRU/LSTM. Gradient clipping. Seq2seq

• Seminar: char-rnn and simple seq2seq

  • HSE homework deadline 23.59 5.04.17
  • YSDA deadline 23.59 9.04.2017

• week7 Partially observable MDPs (under construction now, on-campus 3-6.04.2017)

• Lecture: POMDP intro. Model-based solvers. RNN solvers. RNN tricks: attention, problems with normalization methods, pre-training.

• Seminar: Deep kung-fu & doom with recurrent A3C and DRQN

  • HSE homework deadline 23.59 16.04.17 (kung fu assignment is worth 6pts isntead of 3)

• week 8 Case studies 1 (approx 10.04.17)

• Lecture: Reinforcement Learning as a general way to optimize non-differentiable loss. Seq2seq tasks: g2p, machine translation, conversation models. Tricks for seq2seq models. KL(p||q) vs KL(q||p) and generative adversarial nets.

• Seminar: Optimizing Levenshtein distance with seq2seq for g2p

• week 9 RL in Large/Continuous action spaces. (approx 17.04.17)

• Lecture: Continuous action space MDPs. Value-based approach (NAF). Special case algorithms (dpg, svg). Case study:finance. Large discrete action space problem. Action embedding.

• Seminar: Classic Control and BipedalWalker with ddpg Vs qNAF. https://gym.openai.com/envs/BipedalWalker-v2 . Financial bot as bonus track.

• week 10 Trust Region Policy Optimization. (approx 24.05.17)

• Lecture: Trust region policy optimization in detail. NPO/TRPO.

• Seminar: approximate TRPO vs approximate Q-learning for gym box2d envs (robotics-themed).

• week 11 Advanced exploration methods (appox 1.05.17)

• Lecture1: Improved exploration methods (quantile-based, etc.) for bandits. Bayesian approach. Case study: Contextual bandits for RTB.

• Lecture2: Augmented rewards. Density-based models, UNREAL, variational information maximizing exploration, bayesian optimization with BNNs.

• week 12 Advanced RL topics (approx 8.05.17)

• Lecture 1: Hierarchical MDP. MDP Vs real world. Sparse and delayed rewards. When Q-learning fails. Hierarchical MDP. Hierarchy as temporal abstraction. MDP with symbolic reasoning.

• Lecture 2: Knowledge Transfer in RL & Inverse Reinforcement Learning: basics; personalized medical treatment; robotics.

Course materials and teaching by

• Fedor Ratnikov - lectures, seminars, hw checkups
• Alexander Fritsler - lectures, seminars, hw checkups
• Oleg Vasilev - seminars, hw checkups, technical support
• Pavel Shvechikov - lectures, seminars, HW checkups

• Using pictures from http://ai.berkeley.edu/home.html
• Massively refering CS294
• Tensorflow assignments by Scitator
• Other awesome people: see contributors