投降
发表于 2025-3-21 16:22:41
书目名称Reinforcement Learning for Sequential Decision and Optimal Control影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0825942<br><br> <br><br>书目名称Reinforcement Learning for Sequential Decision and Optimal Control读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0825942<br><br> <br><br>
infringe
发表于 2025-3-21 21:22:12
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PAGAN
发表于 2025-3-22 04:13:17
978-981-19-7786-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
forecast
发表于 2025-3-22 05:36:01
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Creditee
发表于 2025-3-22 10:05:21
Model-Free Indirect RL: Monte Carlo,its environment exploration does not need to traverse the whole state space; and it is often less negatively impacted by the violation of the Markov property. However, MC estimation suffers from very slow convergence due to the demand for sufficient exploration and restricted application on episodic and small-scale tasks.
buoyant
发表于 2025-3-22 13:02:30
Miscellaneous Topics, how to learn with fewer samples, how to learn rewards from experts, how to solve multi-agent games, and how to learn from offline data. The state-of-the-art RL frameworks, libraries, and simulation platforms are also briefly described to support the R&D of more advanced RL algorithms.
有其法作用
发表于 2025-3-22 18:55:08
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石墨
发表于 2025-3-22 23:56:11
Principles of RL Problems,o, it generally contains four key elements: state-action samples, a policy, reward signals, and an environment model. In most stochastic tasks, the value function is defined as the expectation of the long-term return, which is used to evaluate how good a policy is. It naturally holds a recursive rel
Inoperable
发表于 2025-3-23 03:03:21
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Accede
发表于 2025-3-23 08:44:13
Model-Free Indirect RL: Temporal Difference, to update the current value function. Therefore, TD learning methods can learn from incomplete episodes or continuing tasks in a step-by-step manner since it can update the value function based on its current estimate. As stated by Andrew Barto and Richard Sutton, if one had to identify one idea as