找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Reinforcement Learning; State-of-the-Art Marco Wiering,Martijn Otterlo Book 2012 Springer-Verlag Berlin Heidelberg 2012 Artificial Intellig

[復(fù)制鏈接]
查看: 6803|回復(fù): 51
樓主
發(fā)表于 2025-3-21 16:55:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Reinforcement Learning
副標(biāo)題State-of-the-Art
編輯Marco Wiering,Martijn Otterlo
視頻videohttp://file.papertrans.cn/826/825932/825932.mp4
概述Covers all important recent developments in reinforcement learning.Very good introduction and explanation of the different emerging areas in Reinforcement Learning.Includes a survey of previous papers
叢書名稱Adaptation, Learning, and Optimization
圖書封面Titlebook: Reinforcement Learning; State-of-the-Art Marco Wiering,Martijn Otterlo Book 2012 Springer-Verlag Berlin Heidelberg 2012 Artificial Intellig
描述.Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade..The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research..Marco Wiering works at the artificial intelligence department of the University of Groningen i
出版日期Book 2012
關(guān)鍵詞Artificial Intelligence; Computational Intelligence; Decision-Theoretic Planning; Dynamic Programming; M
版次1
doihttps://doi.org/10.1007/978-3-642-27645-3
isbn_softcover978-3-642-44685-6
isbn_ebook978-3-642-27645-3Series ISSN 1867-4534 Series E-ISSN 1867-4542
issn_series 1867-4534
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

書目名稱Reinforcement Learning影響因子(影響力)




書目名稱Reinforcement Learning影響因子(影響力)學(xué)科排名




書目名稱Reinforcement Learning網(wǎng)絡(luò)公開度




書目名稱Reinforcement Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Reinforcement Learning被引頻次




書目名稱Reinforcement Learning被引頻次學(xué)科排名




書目名稱Reinforcement Learning年度引用




書目名稱Reinforcement Learning年度引用學(xué)科排名




書目名稱Reinforcement Learning讀者反饋




書目名稱Reinforcement Learning讀者反饋學(xué)科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 21:23:08 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:07:47 | 只看該作者
Least-Squares Methods for Policy Iterationor the overall resulting approximate policy iteration, we provide guarantees on the performance obtained asymptotically, as the number of samples processed and iterations executed grows to infinity. We also provide finite-sample results, which apply when a finite number of samples and iterations are
地板
發(fā)表于 2025-3-22 07:52:28 | 只看該作者
Learning and Using Modelshe types of models used in model-based methods and ways of learning them, as well as methods for planning on these models. In addition, we examine the typical architectures for combining model learning and planning, which vary depending on whether the designer wants the algorithm to run on-line, in
5#
發(fā)表于 2025-3-22 10:01:53 | 只看該作者
Reinforcement Learning in Continuous State and Action Spacesblems and discuss many specific algorithms. Amongst others, we cover gradient-based temporal-difference learning, evolutionary strategies, policy-gradient algorithms and (natural) actor-critic methods. We discuss the advantages of different approaches and compare the performance of a state-of-the-ar
6#
發(fā)表于 2025-3-22 13:27:37 | 只看該作者
Predictively Defined Representations of Stateal system problem, it is particularly useful in a model-based RL context, when an agent must learn a representation of state and a model of system dynamics online: because the representation (and hence all of the model’s parameters) are defined using only statistics of observable quantities, their l
7#
發(fā)表于 2025-3-22 18:19:36 | 只看該作者
8#
發(fā)表于 2025-3-22 23:48:44 | 只看該作者
9#
發(fā)表于 2025-3-23 02:45:51 | 只看該作者
wird. Darüber hinaus sind ihrer überzeugung nach Begabung und Pers?nlichkeit bedeutsam. Nach Darstellung der Studie und einer Interpretation der Ergebnisse werden abschlie?end Konsequenzen für eine nachhaltige Wirksamkeit des Praxissemesters mit dem Format des Forschenden Lernens diskutiert.
10#
發(fā)表于 2025-3-23 08:58:40 | 只看該作者
genen Handlungssituationen, auf die Auseinandersetzung mit Unterrichtsbeobachtungen als Reflexionsfolie für eine theoretisch gestützte Diskussion professionellen Handelns sowie auf den ebenfalls theoriegestützten Entwurf von Handlungsalternativen. Gerahmt wird die eigenst?ndige forschungsbezogene Ak
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-6 08:20
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
饶河县| 永宁县| 东城区| 武陟县| 长垣县| 钦州市| 资阳市| 兴海县| 咸阳市| 隆子县| 六枝特区| 政和县| 翁源县| 丹阳市| 新沂市| 清河县| 宣威市| 泗水县| 富川| 中方县| 项城市| 奉贤区| 车险| 临武县| 蒙自县| 东兰县| 抚顺县| 织金县| 分宜县| 达日县| 若羌县| 淮滨县| 托里县| 颍上县| 托克逊县| 马龙县| 哈巴河县| 凤山县| 定边县| 铜川市| 德惠市|