https://github.com/vmayoral/basic_reinforcement_learning

An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.
https://github.com/vmayoral/basic_reinforcement_learning

ai artificial-intelligence deep-learning deeplearning neural-networks openai-gym q-learning reinforcement-learning tutorial

Last synced: 2 days ago
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An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

• Host: GitHub
• URL: https://github.com/vmayoral/basic_reinforcement_learning
• Owner: vmayoral
• License: gpl-3.0
• Created: 2016-05-08T13:34:29.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-07-14T07:49:28.000Z (over 1 year ago)
• Last Synced: 2025-01-12T17:05:09.272Z (9 days ago)
• Topics: ai, artificial-intelligence, deep-learning, deeplearning, neural-networks, openai-gym, q-learning, reinforcement-learning, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 43.1 MB
• Stars: 1,116
• Watchers: 61
• Forks: 361
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

README

        
Basic Reinforcement Learning (RL)

============================

This repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques.

### Background review

A quick background review of RL is available [here](BACKGROUND.md).

### Tutorials:

- [x] Tutorial 1: [Q-learning](tutorial1/README.md)

- [x] Tutorial 2: [SARSA](tutorial2/README.md)

- [x] Tutorial 3: [Exploring OpenAI gym](tutorial3/README.md)

- [x] Tutorial 4: [Q-learning in OpenAI gym](tutorial4/README.md)

- [x] Tutorial 5: [Deep Q-learning (DQN)](tutorial5/README.md)

- [x] Tutorial 6: [Deep Convolutional Q-learning](tutorial6/README.md)

- [x] Tutorial 7: [Reinforcement Learning with ROS and Gazebo](tutorial7/README.md)

- [ ] ~~Tutorial 8: [Reinforcement Learning in DOOM](tutorial8/README.md)~~ (**unfinished**)

- [x] Tutorial 9: [Deep Deterministic Policy Gradients (DDPG)](tutorial9/README.md)

- [ ] ~~Tutorial 10: [Guided Policy Search (GPS)](tutorial10/README.md)~~ (**unfinished**)

- [ ] Tutorial 11: [A review of different AI techniques for RL](tutorial11/README.md) (**WIP**)

- [x] Tutorial 12: [Reviewing Policy Gradient methods](tutorial12/README.md)

- [ ] ~~Tutorial 13: [Continuous-state spaces with DQN](tutorial13/README.md)~~ (**merged**)

- [x] Tutorial 14: [Benchmarking RL techniques](tutorial14/README.md)

- [ ] ~~Tutorial 15: [Reviewing Vanilla Policy Gradient (VPG)](tutorial15/README.md)~~ (**failed miserably**)

### References:

- Chris Watkins, Learning from Delayed Rewards, Cambridge, 1989 ([thesis](http://www.cs.rhul.ac.uk/home/chrisw/new_thesis.pdf))

- Awesome Reinforcement Learning repository, https://github.com/aikorea/awesome-rl

- Reinforcement learning CS9417ML, School of Computer Science & Engineering, UNSW Sydney, http://www.cse.unsw.edu.au/~cs9417ml/RL1/index.html

- Reinforcement learning blog posts, https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/

- OpenAI gym docs, https://gym.openai.com/docs

- Vincent Bons implementations, https://gist.github.com/wingedsheep

- David Silver's Deep Reinforcement Learning talk, http://videolectures.net/rldm2015_silver_reinforcement_learning/

- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. arXiv preprint arXiv:1606.01540.

- https://sites.google.com/view/deep-rl-bootcamp/lectures

- https://github.com/vmayoral/gym-cryptocurrencies