增强学习-入门导读

2017年1月17日 (二) 10:07Zhenchen讨论 | 贡献的版本

增强学习入门

教材

增强学习 or 强化学习经典教材

  1. Richard S. Sutton, Andrew Barto, An Introduction to Reinforcement Learning, MIT Press, 1998. Intro_RL
  2. 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.
最后修改于2017年1月17日 (星期二) 10:07