| 書(shū)目名稱 | Recent Advances in Reinforcement Learning | | 副標(biāo)題 | 8th European Worksho | | 編輯 | Sertan Girgin,Manuel Loth,Daniil Ryabko | | 視頻video | http://file.papertrans.cn/823/822969/822969.mp4 | | 叢書(shū)名稱 | Lecture Notes in Computer Science | | 圖書(shū)封面 |  | | 描述 | Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e?cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and ?tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe ?eldof reinforcement learning remains vigorous and varied. It is appropriate to re?ect on some of the reasons for this. One is that the ?eld remains focused on a pr- le | | 出版日期 | Conference proceedings 2008 | | 關(guān)鍵詞 | Bayesian filtering; algorithmic learning; classification; decision making; dynamic programming; ensemble | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-540-89722-4 | | isbn_softcover | 978-3-540-89721-7 | | isbn_ebook | 978-3-540-89722-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer-Verlag Berlin Heidelberg 2008 |
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