Reinforcement Learning 2：Survey Paper

強化學習（二）：綜述型論文

2020/04/17

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// Two dimensions of RL [3]。

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// Reinforcement Learning [1]。

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// SARS [1]。

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// Two dimensions of RL [3]。

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// Epsilon-greedy [1]。

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// Monte Carlo [1]。

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// Sarsa and Q-learning [1]。

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// Actor Critic [3]。

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// REINFORCEwb [2]。

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// Actor Critic [2]。

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// Actor Critic [4]。

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// Sarsa [4] 。

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// Actor Critic [4]。

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References

[1] System
Nguyen, Ngoc Duy, Thanh Nguyen, and Saeid Nahavandi. "System design perspective for human-level agents using deep reinforcement learning: A survey." IEEE Access 5 (2017): 27091-27102.
https://ieeexplore.ieee.org/ielx7/6287639/7859429/08119919.pdf?tp=&arnumber=8119919&isnumber=7859429&ref=aHR0cHM6Ly9zY2hvbGFyLmdvb2dsZS5jb20udHcvc2Nob2xhcj9obD16aC1UVyZhc19zZHQ9MCUyQzUmcT1zeXN0ZW0rZGVzaWduK2RlZXArcmVpbmZvcmNlbWVudCZvcT1zeXN0ZW0rZGVzaWduK2RlZXArcmU=

[2] Overview
Yuxi. "Deep reinforcement learning: An overview." arXiv preprint arXiv:1701.07274 (2017).
https://arxiv.org/pdf/1701.07274.pdf

[3] Survey
Arulkumaran, Kai, et al. "Deep reinforcement learning: A brief survey." IEEE Signal Processing Magazine 34.6 (2017): 26-38.
https://www.gwern.net/docs/rl/2017-arulkumaran.pdf

[4] Algorithms
Csaba. "Algorithms for reinforcement learning." Synthesis lectures on artificial intelligence and machine learning 4.1 (2010): 1-103.
https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf