“增强学习-入门导读”版本间的差异
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（→AlphaGo计算机围棋） |
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| 第1行： | 第1行： | ||
=增强学习入门= | =增强学习入门= | ||
==教材== | ==教材== | ||
| − | + | 增强学习 or 强化学习经典教材 | |
| − | + | ||
| − | Algorithms for Reinforcement Learning | + | #An Introduction to Reinforcement Learning |
| + | [http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Intro_RL] | ||
| + | #Algorithms for Reinforcement Learning | ||
[http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf RLAlgsInMDPs] | [http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf RLAlgsInMDPs] | ||
2017年1月15日 (日) 09:17的版本
增强学习入门
教材
增强学习 or 强化学习经典教材
- An Introduction to Reinforcement Learning
- Algorithms for Reinforcement Learning
研究
AlphaGo计算机围棋
- 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.
- 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, Elsevier 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.
