An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.
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
