“增强学习-入门导读”版本间的差异
来自iCenter Wiki
（→神经科学） |
（→计算机围棋与AlphaGo） |
||
| 第11行： | 第11行： | ||
== 计算机围棋与AlphaGo == | == 计算机围棋与AlphaGo == | ||
| + | ===多臂赌博机=== | ||
* 多臂赌博机（mutiarmed bandit problem） | * 多臂赌博机（mutiarmed bandit problem） | ||
| 第16行： | 第17行： | ||
#Algorithms for Infinitely Many-Armed Bandits, nips 2009. | #Algorithms for Infinitely Many-Armed Bandits, nips 2009. | ||
| + | ===蒙特卡洛树搜索=== | ||
* 蒙特卡洛树搜索（Monte-Carlo Tree Search） | * 蒙特卡洛树搜索（Monte-Carlo Tree Search） | ||
| 第23行： | 第25行： | ||
# '''Monte-Carlo tree search and rapid action value estimation in computer Go, Artificial Intelligence, Elsevier 2011.''' | # '''Monte-Carlo tree search and rapid action value estimation in computer Go, Artificial Intelligence, Elsevier 2011.''' | ||
| − | * | + | ===卷积网络下围棋=== |
| + | * 卷积网络 | ||
# Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008. | # Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008. | ||
# '''Training Deep Convolutional Neural Networks to Play Go, ICML 2015.''' | # '''Training Deep Convolutional Neural Networks to Play Go, ICML 2015.''' | ||
| − | + | == 历史性进展 === | |
# Achieving Master Level Play in 9 × 9 Computer Go, AAAI 2008. | # Achieving Master Level Play in 9 × 9 Computer Go, AAAI 2008. | ||
2017年3月18日 (六) 02:21的版本
目录
强化学习
定义
强化学习（Reinforcement Learning）是一种通用的决策框架( decision-making framework)。Agent代理具有采取动作（action）的能力（capacity），每次动作都会影响Agent的未来状态（State），返回一个标量的奖赏信号（reward signal）来量化表示成功与否（success）。强化学习算法的目标（Goal）就是如何采取动作（action）最大化未来的奖赏（future reward）。
通用AI
深度强化学习（Deep Reinforcement Learning, Deep RL）就是把强化学习RL和深度学习DL的结合起来。用强化学习定义目标，用深度学习给出相应的机制，如Q学习等技术，以实现通用人工智能（General Artificial Intelligence）。
研究
计算机围棋与AlphaGo
多臂赌博机
- 多臂赌博机（mutiarmed bandit problem）
- Multi-armed bandits with episode context, AMAI 2011.
- Algorithms for Infinitely Many-Armed Bandits, nips 2009.
蒙特卡洛树搜索
- 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.
卷积网络下围棋
- 卷积网络
- Mimicking Go Experts with Convolutional Neural Networks, ICANN 2008.
- Training Deep Convolutional Neural Networks to Play Go, ICML 2015.
历史性进展 =
- 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.
计算机游戏
- Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves et al. "Human-level control through deep reinforcement learning." Nature 518, no. 7540 (2015): 529-533.
神经科学
- Gadagkar, V., Puzerey, P., Chen, R., Baird-daniel, E., Farhang, A., & Goldberg, J. (2016). Dopamine Neurons Encode Performance Error in Singing Birds. Science, 354(6317), 1278–1282.
参考资料
参考教材
- Richard S. Sutton, Andrew Barto, An Introduction to Reinforcement Learning, MIT Press, 1998. Intro_RL
- Csaba Szepesvari, Algorithms for Reinforcement Learning, Synthesis lectures on artificial intelligence and machine learning 4, no. 1, pp.1-103, 2010. RLAlgsInMDPs
参考课程
UC Berkeley CS 294: Deep Reinforcement Learning, Deep RL
