查看“增强学习-入门导读”的源代码

=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>===

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.