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https://github.com/jrzmnt/awesome-il-rl
A curated list of awesome Imitation and Reinforcement Learning tutorials, projects and communities.
https://github.com/jrzmnt/awesome-il-rl
List: awesome-il-rl
Last synced: about 1 month ago
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A curated list of awesome Imitation and Reinforcement Learning tutorials, projects and communities.
- Host: GitHub
- URL: https://github.com/jrzmnt/awesome-il-rl
- Owner: jrzmnt
- License: cc0-1.0
- Created: 2020-05-23T17:26:38.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-07-18T14:36:21.000Z (about 4 years ago)
- Last Synced: 2024-04-10T17:19:08.759Z (5 months ago)
- Homepage:
- Size: 75.2 KB
- Stars: 1
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-il-rl - A curated list of awesome Imitation and Reinforcement Learning tutorials, projects and communities. (Other Lists / PowerShell Lists)
README
# Awesome Imitation & Reinforcement Learning [](https://github.com/sindresorhus/awesome)
## Table of Contents
* :books: **[Books](#books)**
* :pencil2: **[Courses](#courses)**
* :video_camera: **[Videos and Lectures](#videos-and-lectures)**
* :pencil: **[Papers](#papers)**
* :blue_book: **[Tutorials](#tutorials)**
* :nerd_face: **[Researchers](#researchers)**
* :computer: **[Websites](#websites)**
* :game_die: **[Environments](#environments)**
* :bar_chart: **[Datasets](#datasets)**
* :loudspeaker: **[Conferences](#Conferences)**
* :vertical_traffic_light: **[Frameworks](#frameworks)**
* :toolbox: **[Tools](#tools)**
* :satellite: **[Miscellaneous](#miscellaneous)**
* :bulb: **[Contributing](#contributing)**
---
### Books
1. [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2020.pdf) by Richard S. Sutton and Andrew G. Barto
2. [An Algorithmic Perspective on Imitation Learning](https://arxiv.org/ftp/arxiv/papers/1811/1811.06711.pdf)
---
### Courses
1. [Fundamentals of Reinforcement Learning](https://www.coursera.org/learn/fundamentals-of-reinforcement-learning) by Martha White and Adam White in Coursera
2. [Sample-based Learning Methods](https://www.coursera.org/learn/sample-based-learning-methods?) by Martha White and Adam White in Coursera
3. [Prediction and Control with Function Approximation](https://www.coursera.org/learn/prediction-control-function-approximation?) by Martha White and Adam White in Coursera
4. [A Complete Reinforcement Learning System (Capstone)](https://www.coursera.org/learn/complete-reinforcement-learning-system?) by Martha White and Adam White in Coursera
5. [Reinforcement Learning - Georgia Tech](https://classroom.udacity.com/courses/ud600)
6. [RL Course by David Silver (DeepMind)](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZBiG_XpjnPrSNw-1XQaM_gB)
---
### Videos and Lectures
1. [Imitation Learning Tutorial ICML 2018](https://youtu.be/WjFdD7PDGw0)
2. [Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning](https://youtu.be/V7CY68zH6ps)
---
### Papers
1. [Augmented Behavioral Cloning from Observation](https://arxiv.org/abs/2004.13529)
2. [Behavioral Cloning from Observation](https://arxiv.org/abs/1805.01954)
3. [Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476)
4. [Generative adversarial Imitation from Observation](https://arxiv.org/abs/1807.06158)
5. [The Arcade Learning Environment: An Evaluation Platform for General Agents](https://paperswithcode.com/paper/the-arcade-learning-environment-an-evaluation)
6. [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://paperswithcode.com/paper/reinforced-cross-modal-matching-and-self)
7. [Imitation Learning: A Survey of Learning Methods](http://www.open-access.bcu.ac.uk/5045/1/Imitation%20Learning%20A%20Survey%20of%20Learning%20Methods.pdf)
8. [Recent Advances in Imitation Learning from Observation](https://arxiv.org/pdf/1905.13566.pdf)
9. [DQN - Playing Atari with Deep Reinforcement Learning](https://arxiv.org/pdf/1312.5602.pdf)
10. [TRPO - Trust Region Policy Optimization](http://proceedings.mlr.press/v37/schulman15.pdf)
11. [SAC - Soft Actor-Critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf)
12. [DDPG - Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf)
---
### Tutorials
1. [example_1](http://example.pdf)
2. [example_2](http://example.pdf)
3. [example_3](http://example.pdf)
---
### Researchers
1. [Richard S. Sutton](http://incompleteideas.net/)
2. [Peter Stone](https://www.cs.utexas.edu/~pstone/)
---
### Websites
1. [Introduction to Imitation Learning](https://blog.statsbot.co/introduction-to-imitation-learning-32334c3b1e7a)
2. [Reinforcement Q-Learning from Scratch in Python with OpenAI Gym](https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/)
---
### Environments
1. [Acrobot-v1](https://gym.openai.com/envs/Acrobot-v1/)
2. [CartPole-v1](https://gym.openai.com/envs/CartPole-v1/)
3. [MountainCar-v0](https://gym.openai.com/envs/MountainCar-v0/)
4. [MountainCarContinuous-v0](https://gym.openai.com/envs/MountainCarContinuous-v0/)
5. [Pendulum-v0](https://gym.openai.com/envs/Pendulum-v0/)
6. [CoinRun](https://openai.com/blog/quantifying-generalization-in-reinforcement-learning/)
---
### Datasets
1. [example_1](http://example.com)
2. [example_2](http://example.com)
3. [example_3](http://example.com)
---
### Conferences
1. [CVPR - IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com)
2. [AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems](http://celweb.vuse.vanderbilt.edu/aamas18/)
3. [IJCAI - International Joint Conference on Artificial Intelligence](https://www.ijcai-18.org/)
4. [AAAI - Association for the Advancement of Artificial Intelligence](https://www.aaai.org/Conferences/conferences.php)
---
### Frameworks
1. [Acme](https://deepmind.com/research/publications/Acme)
2. [Caffe](http://caffe.berkeleyvision.org/)
3. [Torch7](http://torch.ch/)
4. [Theano](http://deeplearning.net/software/theano/)
5. [TensorFlow](https://www.tensorflow.org/)
6. [RLkit](https://github.com/vitchyr/rlkit)
---
### Tools
1. [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
2. [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
---
### Miscellaneous
1. [Deep Reinforcement Learning](https://arxiv.org/pdf/1810.06339.pdf)
-----
### Contributing
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/jrzmnt/Awesome-RL-IL/pulls).
-----
## License
[](http://creativecommons.org/publicdomain/zero/1.0/)