https://github.com/dalmia/david-silver-reinforcement-learning
Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
https://github.com/dalmia/david-silver-reinforcement-learning
artificial-intelligence course-notes gym-environment open-ai python reinforcement-learning
Last synced: 6 months ago
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Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
- Host: GitHub
- URL: https://github.com/dalmia/david-silver-reinforcement-learning
- Owner: dalmia
- License: mit
- Created: 2018-01-10T07:38:51.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-03-31T00:28:42.000Z (over 3 years ago)
- Last Synced: 2025-03-28T09:08:31.466Z (7 months ago)
- Topics: artificial-intelligence, course-notes, gym-environment, open-ai, python, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 21.9 MB
- Stars: 801
- Watchers: 21
- Forks: 213
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# David-Silver-Reinforcement-learning
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This repository contains the notes for the Reinforcement Learning [course](www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) by [David Silver](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Home.html) along with the implementation of the various algorithms discussed, both in Keras (with TensorFlow backend) and [OpenAI](https://openai.com/)'s [gym](https://github.com/openai/gym) framework.
## Syllabus:
- Week 1: Introduction to Reinforcement Learning [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/intro_RL.pdf)][[video](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=1)]
- Week 2: Markov Decision Processes [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf)][[video](https://www.youtube.com/watch?v=lfHX2hHRMVQ&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=2&t=3223s)]
- Week 3: Planning by Dynamic Programming [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf)][[video](https://www.youtube.com/watch?v=Nd1-UUMVfz4&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=3&t=417s)]
- Week 4: Model-Free Prediction [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MC-TD.pdf)][[video](https://www.youtube.com/watch?v=PnHCvfgC_ZA&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=4)]
- Week 5: Model-Free Control [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/control.pdf)][[video](https://www.youtube.com/watch?v=0g4j2k_Ggc4&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=5)]
- Week 6: Value Function Approximation [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/FA.pdf)][[video](https://www.youtube.com/watch?v=UoPei5o4fps&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=6)]
- Week 7: Policy Gradient Methods [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/pg.pdf)][[video](https://www.youtube.com/watch?v=KHZVXao4qXs&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=7)]
- Week 8: Integrating Learning and Planning [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/dyna.pdf)][[video](https://www.youtube.com/watch?v=ItMutbeOHtc&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=8)]
- Week 9: Exploration and Exploitation [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/XX.pdf)][[video](https://www.youtube.com/watch?v=sGuiWX07sKw&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=9)]
- Week 10: Case Study: RL in Classic Games [[slide](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/games.pdf)][[video](https://www.youtube.com/watch?v=kZ_AUmFcZtk&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT&index=10)]
## Dependencies
- TensorFlow
- Keras
- Gym
- Numpy
Install them using [pip](https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjRhLWLnfHYAhVEtY8KHRqfCc4QFggoMAA&url=https%3A%2F%2Fpip.pypa.io%2Fen%2Fstable%2F&usg=AOvVaw18gydNGbBQg6WMxXoxO97K).
## Contributing
Please feel free to create a Pull Request for adding implementations of the algorithms discussed in different frameworks like PyTorch, Caffe, etc. or improving the existing implementations. If you are a beginner, you can refer [this](https://opensource.guide/how-to-contribute/) for getting started.
## Support
If you found this useful, please consider starring(★) the repo so that it can reach a broader audience.
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/dalmia/David-Silver-Reinforcement-learning/blob/master/LICENSE) file for details.
## References
- https://github.com/dennybritz/reinforcement-learning
- https://github.com/llSourcell/AI_for_Video_Games_Syllabus