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README.md

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# Python Reinforcement Learning Book examples
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This is some simple examples for learning Reinforcement Learning
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All the code is python 3.5
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<p align="center"><img width="80%" src="/images/Reinforcement-Learning.png" /></p>
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## Dependencies
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> RLCode팀이 직접 만든 강화학습 예제들을 모아놓은 Repo 입니다.
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>
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> Members - [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)
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[Pull Request](https://github.com/rlcode/reinforcement-learning/pulls)는 언제든 환영입니다.
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문제나 버그, 혹은 궁금한 사항이 있으면 [이슈](https://github.com/rlcode/reinforcement-learning/issues)에 글을 남겨주세요.
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## 필요한 라이브러리들 (Dependencies)
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1. Python 3.5
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2. Tensorflow 1.0.0
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3. Keras
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4. numpy
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5. pandas
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6. matplot
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## 목차
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**강화학습의 기초** - 비교적 단순한 환경인 그리드월드에서 각종 강화학습의 기초를 쌓아봅시다.
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- [정책 이터레이션 (Policy Iteration)](./01_policy_iteration)
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- [가치 이터레이션 (Value Iteration)](./02_value_iteration)
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- [몬테카를로 (Monte Carlo)](./03_monte_carlo)
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- [살사 (SARSA)](./04_sarsa)
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- [큐러닝 (Q-Learning)](./05_q_learning)
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**딥러닝과 강화학습** - 딥러닝을 응용하여 게임같이 좀더 복잡한 환경에서 사람을 이길 수 있는 알고리즘들을 알아보겠습니다.
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- [신경망 (Neural Network)]()
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- [딥 큐러닝 (Deep Q-Learning)]()
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- [Policy Gradient]()

images/Reinforcement-Learning.png

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