“增强学习-入门导读”版本间的差异
来自iCenter Wiki
（→AlphaGo计算机围棋） |
（→AlphaGo计算机围棋） |
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| 第21行： | 第21行： | ||
:Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008. | :Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008. | ||
| − | :Monte-Carlo tree search and rapid action value estimation in computer Go, artificial intelligence, Elsevier 2011. | + | :*Monte-Carlo tree search and rapid action value estimation in computer Go, artificial intelligence, Elsevier 2011. |
:The grand challenge of computer Go Monte Carlo tree search and extensions, cacm 2012. | :The grand challenge of computer Go Monte Carlo tree search and extensions, cacm 2012. | ||
| − | :Training Deep Convolutional Neural Networks to Play Go,icml 2015. | + | :*Training Deep Convolutional Neural Networks to Play Go, icml 2015. |
| − | :Mastering the game of Go with deep neural networks and tree search, nature 2016. | + | :*Mastering the game of Go with deep neural networks and tree search, nature 2016. |
2017年1月14日 (六) 06:57的版本
增强学习入门
教材
An Introduction to Reinforcement Learning Intro_RL
Algorithms for Reinforcement Learning RLAlgsInMDPs
研究
AlphaGo计算机围棋
- 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.
- Achieving Master Level Play in 9 × 9 Computer Go, AAAI 2008.
- Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008.
- Monte-Carlo tree search and rapid action value estimation in computer Go, artificial intelligence, Elsevier 2011.
- The grand challenge of computer Go Monte Carlo tree search and extensions, cacm 2012.
- Training Deep Convolutional Neural Networks to Play Go, icml 2015.
- Mastering the game of Go with deep neural networks and tree search, nature 2016.
