A Study for Improvement for Reinforcement Learning based on Knowledge Sharing Method — Adaptability to a situation of intermingled of complete and incomplete perception under an maze—

2019 ◽  
Vol 9 (2) ◽  
pp. 60-67
Author(s):  
Masashi SUGIMOTO ◽  
MasasHiroya YASHIRO ◽  
Kazuma NISHIMURA ◽  
Shinji TSUZUKI ◽  
Kentarou KURASHIGE ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Sharing

scholarly journals An Improved Reinforcement Learning Algorithm for Cooperative Behaviors of Mobile Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Song ◽  
Yibin Li ◽  
Xiaoli Wang ◽  
Xin Ma ◽  
Jiuhong Ruan
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Knowledge Sharing ◽  
State Space ◽  
Learning Algorithm ◽  
The State ◽  
Convergence Speed ◽  
Exponential Increase ◽  
Cooperative Behaviors ◽  
Reinforcement Learning Algorithm

Reinforcement learning algorithm for multirobot will become very slow when the number of robots is increasing resulting in an exponential increase of state space. A sequentialQ-learning based on knowledge sharing is presented. The rule repository of robots behaviors is firstly initialized in the process of reinforcement learning. Mobile robots obtain present environmental state by sensors. Then the state will be matched to determine if the relevant behavior rule has been stored in the database. If the rule is present, an action will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new rule will be appended to the database. The robots learn according to a given sequence and share the behavior database. We examine the algorithm by multirobot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the convergence speed.

The Knowledge Sharing Based Reinforcement Learning Algorithm for Collective Behaviors of Mobile Robots

2012 ◽  
Vol 588-589 ◽  
pp. 1515-1518
Author(s):  
Yong Song ◽  
Bing Liu ◽  
Yi Bin Li
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Knowledge Sharing ◽  
State Space ◽  
Learning Algorithm ◽  
Collective Behaviors ◽  
Q Learning ◽  
Exponential Increase ◽  
Multi Robot ◽  
Reinforcement Learning Algorithm

Reinforcement learning algorithm for multi-robot may will become very slow when the number of robots is increasing resulting in an exponential increase of state space. A sequential Q-learning base on knowledge sharing is presented. The rule repository of robots behaviors is firstly initialized in the process of reinforcement learning. Mobile robots obtain present environmental state by sensors. Then the state will be matched to determine if the relevant behavior rule has been stored in database. If the rule is present, an action will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new rule will be joined in the database. The robots learn according to a given sequence and share the behavior database. We examine the algorithm by multi-robot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the convergence speed.

Knowledge Sharing: Wissen für den Pflegealltag

PADUA ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. 265-267
Author(s):  
Sabine Bohnet-Joschko
Keyword(s):  
Knowledge Sharing

Zusammenfassung. Gesundheits- und Pflegeberufe gehören zu den wissensintensiven Dienstleistungsberufen, in denen einmal Erlerntes schnell an Aktualität verliert. So können klassische Fort- und Weiterbildungskonzepte die Dynamik der Wissensentwicklung in der Pflege kaum noch abbilden. Insbesondere für Führungskräfte gilt es, trotz zunehmender Arbeitsverdichtung eine Kultur des lebenslangen Lernens für Pflegende zu fördern. Das in den USA durchaus verbreitete, im deutschsprachigen Raum dagegen nahezu unbekannte Konzept «Lunch and Learn» soll hier vorgestellt werden.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models

Sharing With Whom? Processes in Internal and External Knowledge Sharing

2011 ◽  
Author(s):  
Katherine Giuca ◽  
John Schaubroeck ◽  
Abraham Carmeli ◽  
Roy Gelbard
Keyword(s):  
Knowledge Sharing ◽  
External Knowledge
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