GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → yandexdataschool → Practical_rl

yandexdataschool / Practical_rl

Licence: unlicense
A course in reinforcement learning in the wild

Programming Languages

Jupyter Notebook
11667 projects
python
139335 projects - #7 most used programming language

Labels

hacktoberfestdeep-learningpytorchtensorflowkerasreinforcement-learningdeep-reinforcement-learningtheanomooccourse-materialspytorch-tutorialslasagnegit-course

Projects that are alternatives of or similar to Practical rl

Gdrl
Grokking Deep Reinforcement Learning
Stars: ✭ 304 (-93.59%)
Mutual labels:  jupyter-notebook, pytorch-tutorials, reinforcement-learning, deep-reinforcement-learning
Practical dl
DL course co-developed by YSDA, HSE and Skoltech
Stars: ✭ 1,006 (-78.78%)
Mutual labels:  jupyter-notebook, course-materials, lasagne, theano
2048 Deep Reinforcement Learning
Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning
Stars: ✭ 169 (-96.44%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Pytorch sac
PyTorch implementation of Soft Actor-Critic (SAC)
Stars: ✭ 174 (-96.33%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Lagom
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Stars: ✭ 364 (-92.32%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Deep Reinforcement Learning
Repo for the Deep Reinforcement Learning Nanodegree program
Stars: ✭ 4,012 (-15.38%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Advanced Deep Learning And Reinforcement Learning Deepmind
🎮 Advanced Deep Learning and Reinforcement Learning at UCL & DeepMind | YouTube videos 👉
Stars: ✭ 121 (-97.45%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Btgym
Scalable, event-driven, deep-learning-friendly backtesting library
Stars: ✭ 765 (-83.86%)
Mutual labels:  hacktoberfest, reinforcement-learning, deep-reinforcement-learning
Deep Learning Python
Intro to Deep Learning, including recurrent, convolution, and feed forward neural networks.
Stars: ✭ 94 (-98.02%)
Mutual labels:  jupyter-notebook, lasagne, theano
Agentnet
Deep Reinforcement Learning library for humans
Stars: ✭ 298 (-93.71%)
Mutual labels:  reinforcement-learning, lasagne, theano
Drq
DrQ: Data regularized Q
Stars: ✭ 268 (-94.35%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Reinforcement learning tutorial with demo
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
Stars: ✭ 442 (-90.68%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Pytorch Rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Stars: ✭ 121 (-97.45%)
Mutual labels:  jupyter-notebook, pytorch-tutorials, reinforcement-learning
Reinforcementlearning Atarigame
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
Stars: ✭ 118 (-97.51%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Rl Quadcopter
Teach a Quadcopter How to Fly!
Stars: ✭ 124 (-97.38%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Psgan
Periodic Spatial Generative Adversarial Networks
Stars: ✭ 108 (-97.72%)
Mutual labels:  jupyter-notebook, lasagne, theano
Applied Reinforcement Learning
Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
Stars: ✭ 229 (-95.17%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Rl Course Experiments
Stars: ✭ 73 (-98.46%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
Deeplearning2020
course materials for introduction to deep learning 2020
Stars: ✭ 90 (-98.1%)
Mutual labels:  jupyter-notebook, mooc, course-materials
Rad
RAD: Reinforcement Learning with Augmented Data
Stars: ✭ 268 (-94.35%)
Mutual labels:  jupyter-notebook, reinforcement-learning, deep-reinforcement-learning
View All Similar Projects ➔

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 networks. 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

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].