=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. [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]
==研究==
===<B>AlphaGo计算机围棋</B>AlphaGo 计算机围棋 ===
* 蒙特卡洛树搜索（Monte-Carlo tree search）Tree Search）
:# Bandit based monte-carlo planning, ecml 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.'''
:Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, CG 2006.* 神经网络
:Combining Online and Offline Knowledge in UCT# Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008.# '''Training Deep Convolutional Neural Networks to Play Go, ICML 20072015.'''
* 进展
*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 CACM 2012. *# '''Mastering the game of Go with deep neural networks and tree search, nature Nature 2016.'''