2019-OSS-Summer-RLBasic

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2019-OSS-Summer-RLBasic

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2019년 OSS 개발자 포럼 여름 캠프 - 강화학습 기초 관련 예제 코드

2019-OSS-Summer-RLBasic 최신버젼 다운로드

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# 2019-OSS-Summer-RLBasic

2019-OSS-Summer-RLBasic is the material(lecture notes, examples and assignments) repository for reinforcement learning basic course that I have taught at Kookmin University in the summer of 2019. Note that examples and assignments in this repository uses [Keras](https://keras.io/).

## Related Repositories

- [2020-OSS-Winter-AlphaZero](https://github.com/utilForever/2020-OSS-Winter-AlphaZero)

## Contents

- Lecture
    - Day 1
        - What is Reinforcement Learning?
        - MDP (Markov Decision Process)
            - State
            - Action
            - Reward Function
            - State Transition Probability
            - Discount Rate
            - Policy
        - Value Function and Q-Function
        - Bellman Equation
            - Bellman Expectation Equation
            - Bellman Optimality Equation
        - Dynamic Programming
            - Policy Iteration
            - Value Iteration
        - Policy Evaluation
            - Monte-Carlo Prediction
            - Temporal-Difference Prediction
        - SARSA
        - Q-Learning
    - Day 2
        - Approximation Function
        - Neural Network
            - Node and Activation Function
            - Deep Learning
        - Deep SARSA
        - Policy Gradient
            - Policy-based Reinforcement Learning
            - REINFORCE
        - DQN (Deep Q-Network)
- Examples
    - Day 1 (Grid World)
        - Policy Iteration
        - Value Iteration
        - Monet-Carlo
        - SARSA
        - Q-Learning
    - Day 2 (Grid World and CartPole-v0)
        - Deep SARSA
        - REINFORCE
        - DQN
        - Actor-Critic
- Assignments
    - Day 1 (Maze)
        - SARSA
        - Q-Learning
    - Day 2 (LunarLander-v0)
        - DQN

## Acknowledgement

This course uses the contents of [파이썬과 케라스로 배우는 강화학습(Wikibooks, 2017)](https://wikibook.co.kr/reinforcement-learning/) for lecture notes and [RLCode team repository](https://github.com/rlcode/reinforcement-learning-kr) for example codes. Thanks to maintainers ([이웅원](https://github.com/dnddnjs), [이영무](https://github.com/zzing0907), [양혁렬](https://github.com/Hyeokreal), [이의령](https://github.com/wooridle), [김건우](https://github.com/keon)).

Also, I prepared this materials with teaching assistant [Junyeong Park](https://github.com/JYPark09), and [Hyeonsu Kim](https://github.com/git-rla). Thanks! :D

## How To Contribute

Contributions are always welcome, either reporting issues/bugs or forking the repository and then issuing pull requests when you have completed some additional coding that you feel will be beneficial to the main project. If you are interested in contributing in a more dedicated capacity, then please contact me.

## Contact

You can contact me via e-mail (utilForever at gmail.com). I am always happy to answer questions or help with any issues you might have, and please be sure to share any additional work or your creations with me, I love seeing what other people are making.

## License

The class is licensed under the [MIT License](http://opensource.org/licenses/MIT):

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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