By a News Reporter-Staff News Editor at Computers, Networks & Communications -- A new study on Reinforcement Learning is now available. According to news reporting from Shaanxi, People’s Republic of China, by VerticalNews journalists, research stated, “A robot soccer system is a typical complex time-sequence decision-making system. Problems of uncertain knowledge representation and complex models always exist in robot soccer games.”
Financial supporters for this research include National research and development plan of China, National Natural Science Foundation of Shaanxi, Fundamental Research Funds for the Central Universities.
The news correspondents obtained a quote from the research from Northwestern Polytechnic University, “To achieve an adaptive decision-making mechanism, a method with fuzzy Bayesian reinforcement learning (RL) is proposed in this paper. To extract the features utilized in the proposed learning method, a fuzzy comprehensive evaluation method (FCEM) is developed. This method classifies the situations in robot soccer games into a set of features. With the fuzzy analytical hierarchy process (FAHP), the FCEM can calculate the weights according to defined factors for these features, which comprise the dimensionality of the state space. The weight imposed on each feature determines the range of each dimension. Through a Bayesian network, the comprehensively evaluated features are transformed into decision bases. An RL method for strategy selection over time is implemented. The fuzzy mechanism can skillfully adapt experiences to the learning system and provide flexibility in state aggregation, thus improving learning efficiency.”
According to the news reporters, the research concluded: “The experimental results demonstrate that the proposed method has better knowledge representation and strategy selection than other competing methods.”
For more information on this research see: An adaptive decision-making method with fuzzy Bayesian reinforcement learning for robot soccer. Information Sciences , 2018;436():268-281. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier - www.elsevier.com; Information Sciences - http://www.journals.elsevier.com/information-sciences/)
Our news journalists report that additional information may be obtained by contacting H.B. Shi, Northwestern Polytechnic Univ, Sch Comp Sci, Xian 710072, Shaanxi, People’s Republic of China. Additional authors for this research include Z.Q. Lin, S.G. Zhang, X.S. Li and K.S. Hwang.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.ins.2018.01.032. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-12), Findings from Northwestern Polytechnic University in Reinforcement Learning Reported (An adaptive decision-making method with fuzzy Bayesian reinforcement learning for robot soccer), Computers, Networks & Communications, 178, ISSN: 1944-1568, BUTTER® ID: 015460354
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