Reinforcement Learning 1：An Introduction

強化學習（一）：簡介

2020/04/17

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Demystifying Deep Reinforcement Learning | Computational Neuroscience Lab
https://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/

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Deep Reinforcement Learning

TD（Q-learning）：DQN、DDQN、DNA、NAF、C51、QR-DQN、HER、DQfD、Rainbow。
AC（Actor-Critic）：A3C（A2C）、（DRQN）UNREAL、DPG、DDPG、TD3、SAC、ACKTR。
PG（REINFORCE）：TRPO、PPO、PDO、CPO、IPO。

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// AlphaGo [1]。

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// DQN [1]。

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// NAS-RL [2]。

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// SARS [1]。

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Demystifying Deep Reinforcement Learning | Computational Neuroscience Lab
https://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/

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Q-Learning : A Maneuver of Mazes - Becoming Human: Artificial Intelligence Magazine
https://becominghuman.ai/q-learning-a-maneuver-of-mazes-885137e957e4

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My Journey to Reinforcement Learning — Part 1: Q-Learning with Table
https://towardsdatascience.com/my-journey-to-reinforcement-learning-part-1-q-learning-with-table-35540020bcf9

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Q-Learning : A Maneuver of Mazes - Becoming Human: Artificial Intelligence Magazine
https://becominghuman.ai/q-learning-a-maneuver-of-mazes-885137e957e4

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Introduction to Reinforcement Learning — Deep Reinforcement Learning for Hackers (Part 0)
https://medium.com/@curiousily/getting-your-feet-rewarded-deep-reinforcement-learning-for-hackers-part-0-900ca5bb83e5

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Q-learning - Wikipedia
https://en.m.wikipedia.org/wiki/Q-learning

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Introduction to Deep Q-Learning for Reinforcement Learning (in Python)
https://www.analyticsvidhya.com/blog/2019/04/introduction-deep-q-learning-python/

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// Sarsa [3]。

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// Q-learning [3]。

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// Sarsa and Q-learning [4]。

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Deep-Learning-Papers-Reading-Roadmap/README.md at master · floodsung/Deep-Learning-Papers-Reading-Roadmap · GitHub
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md

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// 強化學習演進路線 [5]。

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// 一些強化學習的演算法 [6], [7]。

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[1708.07902] Deep Learning for Video Game Playing
https://arxiv.org/abs/1708.07902

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[1910.09615] IPO: Interior-point Policy Optimization under Constraints
https://arxiv.org/abs/1910.09615

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[1910.09615] IPO: Interior-point Policy Optimization under Constraints
https://arxiv.org/abs/1910.09615

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用Python實作強化學習｜使用TensorFlow與OpenAI Gym
http://books.gotop.com.tw/v_ACD017800

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GitHub - Curt-Park/rainbow-is-all-you-need: Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow
https://github.com/Curt-Park/rainbow-is-all-you-need

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GitHub - MrSyee/pg-is-all-you-need: Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.
https://github.com/MrSyee/pg-is-all-you-need

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References

[1] 強化學習 Reinforcement Learning
https://www.slideshare.net/mobile/yenlung/reinforcement-learning-90737484

[2] [論文閱讀]Neural Architecture Search with Reinforcement Learning – AMMAI
https://sss050531.wordpress.com/2018/06/09/論文閱讀neural-architecture-search-with-reinforcement-learning/

[3] 强化学习(七)--Q-Learning和Sarsa - 知乎
https://zhuanlan.zhihu.com/p/46850008

[4] artificial intelligence - What is the difference between Q-learning and SARSA? - Stack Overflow
https://stackoverflow.com/questions/6848828/what-is-the-difference-between-q-learning-and-sarsa

[5] 强化学习演进路线 - 知乎
https://zhuanlan.zhihu.com/p/49429128

[6] Reinforcement Learning algorithms — an intuitive overview
https://medium.com/@SmartLabAI/reinforcement-learning-algorithms-an-intuitive-overview-904e2dff5bbc

[7] Part 2: Kinds of RL Algorithms — Spinning Up documentation
https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html