【资源】Python强化学习实战，Anaconda公司的高级数据科学家讲解（附相关Python开源库）

【导读】Christine Doig是Anaconda公司的高级数据科学家。没错Anaconda就是那个著名的Python科学计算与发行管理软件。Christine Doig从最基本的强化学习概念开始介绍强化学习Python实践经验，并以强化学习中的经典任务--Cartpole问题作为学习的入门例子，讲解从环境搭建、模型训练再到最后的效果评估的结果。

▌简介

Cartpole描述的问题可以认为是：在一辆小车上竖立一根杆子，然后给小车一个推或者拉的力，使得杆子尽量保持平衡不滑倒。

更详细的描述可参见openai官网上关于Cartpole问题的解释：https://gym.openai.com/envs/CartPole-v0

▌强化学习用到的python库

• OpenAI Gym: Toolkit for developing and comparing reinforcement learningalgorithms. MIT License, Last commit: November 2017 baselines: high-quality implementations of reinforcement learning algorithms,MIT License, Last commit: November 2017
• TensorForce, A TensorFlow library for applied reinforcement learning, Apache 2,Last commit: November 2017
• DeepRL, Highly modularized implementation of popular deep RL algorithms byPyTorch, Apache 2 License, Last commit: November 2017
• RLlab, a framework for developing and evaluating reinforcement learningalgorithms, MIT License, Last commit: July 2017
• AgentNet, Python library for deep reinforcement learning usingTheano+Lasagne, MIT License, Last commit: August 2017
• RLPy, the Reinforcement Learning Library for Education and Research,3-Clause BSD License, Last commit: April 2016.
• PyBrain, the Python Machine Learning Library, 3-Clause BSD License, Lastcommit: March 2016.

▌强化学习资源

• Reinforcement Learning courseby David Silver http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
• https://blog.acolyer.org/2017/11/17/mastering-the-game-of-go-without-humanknowledge/
• https://keon.io/deep-q-learning/
• https://rishav1.github.io/reinlearning/2017/01/05/simple-swarm-intelligenceoptimization-for-cartpole-balancing-problem.html
• AlphaGo Zero's win, what itmeans, Fast Forward Labs: http:// blog.fastforwardlabs.com/2017/10/25/alphago-zero.html
• 更多可以查看专知以前推出的强化学习荟萃资料：

【专知荟萃23】深度强化学习RL知识资料全集（入门/进阶/论文/综述/代码/专家，附查看）

▌PPT内容

参考链接：

https://speakerdeck.com/chdoig/rl-pytexas-2017

▌特别提示-Python强化学习实战 PPT下载：

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