/Practical_RL

A course in reinforcement learning in the wild

Primary LanguageJupyter NotebookMIT LicenseMIT

Practical_RL Fall 2017

A course on reinforcement learning in the wild. Taught on-campus at HSE(russian) and maintained to be friendly to online students (both english and russian).

Note: This branch follows the live course held in HSE during fall'17 (probably right now). For full content from previous iteration, go here.

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 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!

Enrollment guide

HSE classes are happening on mondays, 18-10 till 21-00. [lecture 317, seminar 503]

Everyone who wants to attend the course on-campus ping jheuristic@yandex-team.ru

  1. All course materials are in this repository.
  2. Join telegram chat https://t.me/practicalrl_fall2017
  3. Homework submission (only HSE students): Enroll to http://anytask.org/course/228 with invite code lmQLmU3
  4. Join piazza (questions and announcement service) https://piazza.com/cs_hse/fall2017/rl101/home with access code rl101
  5. (only HSE students) Read course rules

Course info

RL reading group

Announcements

  • 2017.10.02 - week4 homework is yet to be published, week3 and week4 deadlines are shifted one week into the future.
  • 2017.09.24 - Week3 homework published, we're sorry for the delay
  • 2017.09.13 - Gym website seems to have gone down indefinitely. Therefore,
  • week0 homework: Bonus I counts as 2 points if you beat mean reward +5.0 for Taxi-v1 or +0.95 on frozenlake8x8
  • week1 homework: Instead of 1 point for task 2.2 and 3 points for 2.3 you get 4 points for 2.3.
  • Since you can't submit, just ignore and instructions to do so. We'll push them this weekend to avoid merge conflicts for students.
  • 2017.09.04 - first class just happened. Anytask submission form TBA

Syllabus

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

  • week0 Welcome to Reinforcement Learning

    • Lecture: RL problems around us. Decision processes. Basic genetic algorithms
    • Seminar: Welcome into openai gym, basic genetic algorithms
    • Homework description - see week0/README.md
      • HSE Homework deadline: 23.59 15.09.17

  • week1 RL as blackbox optimization

    • Lecture: Recap on genetic algorithms; Evolutionary strategies. Stochastic optimization, Crossentropy method. Parameter space search vs action space search.
    • Seminar: Tabular CEM for Taxi-v0, deep CEM for box2d environments.
    • Homework description - see week1/README.md
      • HSE Homework deadline: 23.59 22.09.17

  • week2 Value-based methods

    • Lecture: Discounted reward MDP. Value-based approach. Value iteration. Policy iteration. Discounted reward fails.
    • Seminar: Value iteration.
      • HSE Homework deadline: 23.59 29.09.17

  • week3 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
    • HSE Homework deadline: _23.59 13.10.17

  • week3.5 - deep learning recap

    • Lecture: Deep learning 101
    • Seminar: Simple image classification with convnets
    • HSE Homework deadline: _23.59 13.10.17

  • week4 Approximate reinforcement learning

    • 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)
    • HSE Homework deadline: _23.59 20.10.17

  • week5 Exploration in reinforcement learning

    • 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.

  • week6 Policy gradient methods I

    • 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

[Recurrent neural nets recap goes here]

  • week7 Partially observable MDPs

    • Lecture: POMDP intro. POMDP learning (agents with memory). POMDP planning (POMCP, etc)
    • Seminar: Deep kung-fu & doom with recurrent A3C and DRQN

  • week8 Applications II

    • Lecture: Reinforcement Learning as a general way to optimize non-differentiable loss. G2P, machine translation, conversation models, image captioning, discrete GANs. Self-critical sequence training.
    • Seminar: Simple neural machine translation with self-critical sequence training

  • week9 Policy gradient methods II

    • Lecture: Trust region policy optimization. NPO/PPO. Deterministic policy gradient. DDPG. Bonus: DPG for discrete action spaces.
    • Seminar: Approximate TRPO for simple robotic tasks.

[Maybe a bonus lecture here]

Course staff

Course materials and teaching by

Contributions

fall17 changes

  • Better support for tensorflow & pytorch
  • Our notation is now compatible with Sutton's
  • Reworked & reballanced some assignments
  • Added more practice on model-based RL