增强学习-入门导读

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）

1. Bandit based monte-carlo planning, ECML 2006.
2. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, CG 2006.
3. Combining Online and Offline Knowledge in UCT, ICML 2007.
4. Monte-Carlo tree search and rapid action value estimation in computer Go, Artificial Intelligence, Elsevier 2011.

• 神经网络

1. Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008.
2. Training Deep Convolutional Neural Networks to Play Go, ICML 2015.

• 进展

1. Achieving Master Level Play in 9 × 9 Computer Go, AAAI 2008.
2. The grand challenge of computer Go Monte Carlo tree search and extensions, CACM 2012.
3. Mastering the game of Go with deep neural networks and tree search, Nature 2016.