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? 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)
• English chat -
• How to submit homeworks[HSE and YSDA only]: anytask instructions and grading rules
• E-mail for everything else : practicalrl17@gmail.com (please don't submit homeworks via e-mail)
• Anonymous feedback form for everything that didn't go through e-mail.
• About the course

• 17.02.17 - warning! we force-pushed into the repository. Please back-up your github files before you pull!
• 16.02.17 - Lecture slides are now available through urls in README files for each week like this. You can also find full archive here.
• 16.02.17 - HSE homework 3 added
• 14.02.17 - HSE deadlines for weeks 1-2 extended!
• 14.02.17 - anytask invites moved here
• 14.02.17 - if you're from HSE track and we didn't reply to your week0 homework submission, raise panic!
• 11.02.17 - week2 success thresholds are now easier: get >+50 for LunarLander or >-180 for MountainCar. Solving env will yield bonus points.
• 13.02.17 - Added invites for anytask.org
• 10.02.17 - from now on, we'll formally describe homework and add useful links via ./week*/README.md files. Example.
• 9.02.17 - YSDA track started
• 7.02.17 - HWs checked up
• 6.02.17 - week2 uploaded
• 27.01.17 - merged fix by omtcyfz, thanks!
• 27.01.17 - added course mail for homework submission: practicalrl17@gmail.com
• 23.01.17 - first class happened
• 23.01.17 - created repo

• 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 Monte-carlo methods

• 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.

  • 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 - see ./week2/README.md
  • HSE homework deadline: 23.59 15.02.17

• 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

  • HSE homework deadline 23.59 22.02.17

• 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
  • HSE homework deadline 23.59 1.03.17

• week4 Approximate reinforcement learning

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

• Seminar: Approximate Q-learning. (CartPole, MountainCar, Breakout)

• week i+1 Deep reinforcement learning

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

• Seminar: Playing atari with deep reinforcement learning. Experience replay. (classwork = doombasic)

• week i+1 Policy-based methods

• Lecture: Motivation for policy-based, policy gradient, logderivative trick, REINFORCE/crossentropy method, variance theorem(advantage), advantage actor-critic (incl.n-step advantage), off-policy actor-critic (off-PAC), natural gradients(briefly), continuous action space(teaser).

• Seminar: a2c Vs qlearning for MountainCar/Doom, entropy regularization & tricks.

• week i+1 Trust Region Policy Optimization.

• Lecture: Trust region policy optimization in detail.

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

• week i+1 Large/Continuous action space. Case study: recsys.

• Lecture: Continuous action space MDPs. Model-based approach (NAF). Actor-critic approach (dpg, svg). Trust Region Policy Optimization. Large discrete action space problem. Action embedding.

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

somewhere here RNN crash-course

• week i+1 Partially observable MDPs

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

• Seminar: Deep kung-fu with recurrent A2C vs feedforward A2C

• week i+1 Advanced exploration methods: intrinsic motivation

• Lecture: Augmented rewards. Heuristics (UNREAL,density-based models), formal approach: information maximizing exploration. Model-based tricks(also refer mcts).

• Seminar: Vime vs epsilon-greedy for Go9x9 (bonus 19x19)

• week i+1 Advanced exploration methods: probablistic approach.

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

• Seminar: Bandits

• week i+1 Case studies I

• Lecture: Reinforcement Learning as a general way to optimize non-differentiable loss. KL(p||q) vs KL(q||p). Case study: machine ranslation, speech synthesis, conversation models.

• Seminar: Optimizing Levenstein for word transcription

• week i+1 Hierarchical MDP

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

• Seminar: Hierarchical RL for atari games with rare rewards (starting from pre-trained DQN)

• week i+1 Case studies II

• Lecture: Direct policy optimization: finance. Inverse Reinforcement Learning: personalized medial treatment, robotics.

• Seminar: Portfolio optimization as POMDP.

Course materials and teaching by

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

• Using pictures from http://ai.berkeley.edu/home.html
• Other contributions: omtcyfz dmittov arogozhnikov