审美家
发表于 2025-3-21 16:35:22
书目名称Reinforcement Learning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0825930<br><br> <br><br>书目名称Reinforcement Learning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0825930<br><br> <br><br>
散布
发表于 2025-3-21 23:12:07
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Angiogenesis
发表于 2025-3-22 02:33:55
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ABOUT
发表于 2025-3-22 08:31:57
https://doi.org/10.1007/978-1-4615-3618-5agents; algorithms; artificial intelligence; control; learning; machine learning; proving; reinforcement le
magnanimity
发表于 2025-3-22 11:58:11
0893-3405 learner is not told which action to take, asin most forms of machine learning, but instead must discover whichactions yield the highest reward by trying them. In the mostinteresting and challenging cases, actions may affect not only theimmediate reward, but also the next situation, and through that
我们的面粉
发表于 2025-3-22 14:24:18
Technical Note,he action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.
Mettle
发表于 2025-3-22 20:23:27
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酷热
发表于 2025-3-22 21:41:06
Introduction: The Challenge of Reinforcement Learning,m. In the most interesting and challenging cases, actions may affect not only the immediate’s reward, but also the next situation, and through that all subsequent rewards. These two characteristics—trial-and-error search and delayed reward—are the two most important distinguishing features of reinforcement learning.
放肆的我
发表于 2025-3-23 02:36:48
Book 1992 not told which action to take, asin most forms of machine learning, but instead must discover whichactions yield the highest reward by trying them. In the mostinteresting and challenging cases, actions may affect not only theimmediate reward, but also the next situation, and through that allsubsequ
Ceremony
发表于 2025-3-23 08:20:02
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