“增强学习-入门导读”版本间的差异
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增强学习 or 强化学习经典教材 | 增强学习 or 强化学习经典教材 | ||
| − | #Richard S. Sutton, Andrew Barto, An Introduction to Reinforcement Learning, MIT Press, 1998. | + | #Richard S. Sutton, Andrew Barto, An Introduction to Reinforcement Learning, MIT Press, 1998. [http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Intro_RL] |
| − | [http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Intro_RL] | + | #Csaba Szepesvari, Algorithms for Reinforcement Learning, Synthesis lectures on artificial intelligence and machine learning 4, no. 1, pp.1-103, 2010. [http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf RLAlgsInMDPs] |
| − | #Csaba Szepesvari, Algorithms for Reinforcement Learning, Synthesis lectures on artificial intelligence and machine learning 4, no. 1 | + | |
==研究== | ==研究== | ||
2017年1月17日 (二) 10:07的版本
增强学习入门
教材
增强学习 or 强化学习经典教材
- Richard S. Sutton, Andrew Barto, An Introduction to Reinforcement Learning, MIT Press, 1998. Intro_RL
- Csaba Szepesvari, Algorithms for Reinforcement Learning, Synthesis lectures on artificial intelligence and machine learning 4, no. 1, pp.1-103, 2010. RLAlgsInMDPs
研究
AlphaGo计算机围棋
蒙特卡洛树搜索（Monte-Carlo tree search）
- Bandit based monte-carlo planning, ecml 2006.
- Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, CG 2006.
- Combining Online and Offline Knowledge in UCT, ICML 2007.
- Monte-Carlo tree search and rapid action value estimation in computer Go, artificial intelligence, Elsevier 2011.
神经网络
- Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008.
- Training Deep Convolutional Neural Networks to Play Go, icml 2015.
进展
- Achieving Master Level Play in 9 × 9 Computer Go, AAAI 2008.
- The grand challenge of computer Go Monte Carlo tree search and extensions, cacm 2012.
- Mastering the game of Go with deep neural networks and tree search, nature 2016.
