Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)
International Journal of Intelligent Information Systems
Volume 6, Issue 6, December 2017, Pages: 72-84
Received: Oct. 2, 2017; Accepted: Oct. 17, 2017; Published: Dec. 7, 2017
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Deepak Annasaheb Vidhate, Department of Computer Engineering, College of Engineering, Pune, India
Parag Arun Kulkarni, iKnowlation Research Labs Pvt. Ltd., Pune, India
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The paper gives novel approach Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail application. Furthermore, it put up different cooperation schemes for multiagent cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, EGoal driven and Expert agents scheme. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speedup the collection of agents that achieve excellent action policies. Accordingly this approach presents three retailer stores in the retail market place. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is formed as Markov decision process model that makes it possible to design learning algorithms. The proposed algorithms noticeably learn dynamic consumer performance. Moreover, the paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for the period of one year sale duration. The results obtained by the proposed expert agent based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.
Cooperation Schemes, Multi-Agent Learning, Reinforcement Learning
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Deepak Annasaheb Vidhate, Parag Arun Kulkarni, Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA), International Journal of Intelligent Information Systems. Vol. 6, No. 6, 2017, pp. 72-84. doi: 10.11648/j.ijiis.20170606.12
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Deepak A. Vidhate and Parag Kulkarni, “Expertise Based Cooperative Reinforcement Learning Methods (ECRLM)”, International Conference on Information & Communication Technology for Intelligent System, Springer book series Smart Innovation, Systems and Technologies (SIST, volume 84), Cham, pp 350-360, 2017.
Abhijit Gosavi, “Simulation-based Optimization: Parametric Optimization Techniques and Reinforcement Learning” Kluwer Academic Publishers, 2003.
Andrew Y. Ng, "Sharding and Policy Search in Reinforcement Learning". Ph.D. dissertation. The University of California, Berkeley, 2003.
Deepak A Vidhate and Parag Kulkarni, “Enhanced Cooperative Multiagent Learning Algorithms (ECMLA) using Reinforcement Learning” International Conference on Computing, Analytics and Security Trends (CAST), IEEE Xplorer, pp 556-561, 2017
Antanas Verikas, Arunas Lipnickas, Kerstin Malmqvist, Marija Bacauskiene, and Adas Gelzinis, “Soft Combination of Neural Classifiers: A Comparative Study”, Pattern Recognition Letters No. 20, 1999, pp 429-444.
Deepak A. Vidhate and Parag Kulkarni "Innovative Approach Towards Cooperation Models for Multiagent Reinforcement Learning (CMMARL)" International Conference on Smart Trends for Information Technology and Computer Communications Springer, Singapore, pp. 468-478, 2016.
Babak Nadjar Araabi, Sahar Mastoureshgh, and Majid Nili Ahmadabadi “A Study on Expertise of Agents and Its Effects on Cooperative Q Learning” IEEE Transactions on Evolutionary Computation, vol: 14, pp: 23-57, 2011.
C. J. C. H. Watkinsand P. Dayan, “Qlearning”, Machine Learning, 8(3): 1992.
Deepak A. Vidhate and Parag Kulkarni, “Implementation of Multiagent Learning Algorithms for Improved Decision Making”, International Journal of Computer Trends and Technology (IJCTT), Volume 35 Number 2- May 2016.
Deepak A Vidhate and Parag Kulkarni, “Enhancement in Decision Making with Improved Performance by Multiagent Learning Algorithms” IOSR Journal of Computer Engineering, Vol. 1, No. 18, pp 18-25, 2016.
Ju Jiang and Mohamed S. Kamel “Aggregation of Reinforcement Learning Algorithms” International Joint Conference on Neural Networks, Vancouver, Canada July 16-21, 2006.
Lun-Hui Xu, Xin-Hai Xia and Qiang Luo “The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment” Mathematical Problems in Engineering, Hindawi Publishing Corporation, Volume 2013.
Deepak A. Vidhate and Parag Kulkarni “New Approach for Advanced Cooperative Learning Algorithms using RL methods (ACLA)” Vision Net’16 Proceedings of the Third International Symposium on Computer Vision and the Internet, ACM DL pp 12-20, 2016.
M. Kamel and N. Wanas, “Data Dependence in Combining Classifiers”, Multiple Classifiers Systems, Fourth International Workshop, Surrey, UK, June 11-13, 2003, pp 1-14.
Deepak A Vidhate and Parag Kulkarni, "Performance enhancement of cooperative learning algorithms by improved decision-making for context based application", International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) IEEE Xplorer, pp 246-252, 2016.
Deepak A. Vidhate and Parag Kulkarni, “Design of Multiagent System Architecture based on Association Mining for Cooperative Reinforcement Learning”, Spvryan's International Journal of Engineering Sciences & Technology (SEST), Volume 1, Issue 1, 2014.
V. L. Raju Chinthalapati, Narahari Yadati, and Ravikumar Karumanchi, “Learning Dynamic Prices in Multi Seller Electronic Retail Markets With Price Sensitive Customers, Stochastic Demands, and Inventory Replenishments”, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications & Reviews, Vol. 36, No. 1, Jan 2008.
Y. S. Huang and C. Y. Suen, “A method of combining multiple experts for the recognition of unconstrained hand written numerals.” IEEE Trans. on Pattern Analysis & Machine Intelligence 17(1), pp 90-94, 1995.
Deepak A. Vidhate and Parag Kulkarni, “Multilevel Relationship Algorithm for Association Rule Mining used for Cooperative Learning”, International Journal of Computer Applications (0975–8887), volume 86, number 4, pp 20-27, 2014.
Young-Cheol Choi, Hyo-Sung Ahn “A Survey on Multiagent Reinforcement Learning: Coordination Problems”, IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, pp. 81–86, 2010.
Deepak A. Vidhate, Parag Kulkarni, “To improve association rule mining using new technique: Multilevel relationship algorithm towards cooperative learning”, International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), IEEE pp 241—246, 2014.
Zahra Abbasi, Mohammad Ali Abbasi “Reinforcement Distribution in a Team of Cooperative Q learning Agent”, Proceedings of the 9th ACIS International Conference on Artificial Intelligence, Distributed Computing, IEEE 2012.
Deepak A Vidhate, Parag Kulkarni, “A Novel Approach to Association Rule Mining using Multilevel Relationship Algorithm for Cooperative Learning” Proceedings of 4th International Conference on Advanced Computing & Communication Technologies (ACCT-2014), pp 230-236, 2014.
Deepak A Vidhate, Parag Kulkarni, “Cooperative machine learning with information fusion for dynamic decision making in diagnostic applications”, International Conference on Advances in Mobile Network, Communication and its Applications (MNCAPPS), IEEE, pp 70-74, 2012.
Deepak A Vidhate, Parag Kulkarni, “A Step toward Decision making in Diagnostic Applications using Single Agent Learning Algorithms”, International Journal of Computer Science and Information Technologies (IJCSIT), Volume 7, Number 3, pp 1337-1342, 2016.
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