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Explorations in Computer Go, Web Search, and Online Advertising
Published on Jul 25, 20114316 Views
In computer go, the goal is to find a good move in a given position by exploring the associated game tree, which is far too large to enumerate and hence requires sophisticated methods for navigation.
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Chapter list
Explorations in Computer Go, Web Search, and Online Advertising00:00
Overview - 101:08
Overview - 201:35
Maximising Long-Term Reward is Hard01:37
Exploration-Exploitation Trade-Off07:24
Overview - 308:39
Tabular Q-Learning08:48
Results12:28
Learning Aggressive Fighting13:32
Learning “Aikido” Style Fighting14:37
Lesson 116:18
Overview - 417:03
Reinforcement Learning for Car Racing: AMPS (Kochenderfer, 2005)17:59
Project Gotham Racing 319:43
Balancing Abstraction Complexity19:52
State Representation and Reward19:52
Actions20:21
Project Gotham Racing20:29
Lesson 223:30
Overview - 524:00
Computer Go24:10
The Game of Go24:55
Computer Go - 126:08
Computer Go - 226:52
Key Insights for Monte-Carlo Go28:20
Monte Carlo Go - 130:17
Monte Carlo Go - 231:07
Monte Carlo Go - 331:29
Monte Carlo Go - 431:34
Monte Carlo Go - 531:39
Upper Confidence Intervals32:24
Success of Monte-Carlo Go33:44
Lesson 335:18
Overview - 635:52
Traditional Web Search Paradigm35:56
User Feedback and Clicks38:29
Generalisation across Documents39:44
Diversity of Search Results41:18
Dynamics and Mortality42:06
Lesson 442:50
Overview - 743:12
bing43:13
AdPredictor: Bayesian Probit Regression44:11
The Causal Loop44:20
Thompson Heuristic47:05
Lesson 548:16
Lessons Learnt48:49