“增强学习-入门导读”版本间的差异
来自iCenter Wiki
（→AlphaGo计算机围棋） |
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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. | ||
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| + | 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13日 (五) 11:28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.
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, elseveir 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.
