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README.md

Practical_RL

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).

Manifesto:

  • 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!

Github contributors

Course info

Additional materials

Syllabus

The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.

  • 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.
  • 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.
  • 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.
  • recap_deep_learning - deep learning recap

    • Lecture: Deep learning 101
    • Seminar: Intro to pytorch/tensorflow, simple image classification with convnets
  • 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)
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • week10_planning Model-based RL & Co

    • Lecture: Model-Based RL, Planning in General, Imitation Learning and Inverse Reinforcement Learning
    • Seminar: MCTS for toy tasks
  • yet_another_week Inverse RL and Imitation Learning

    • All that cool RL stuff that you won't learn from this course :)

Course staff

Course materials and teaching by: [unordered]

Contributions

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