Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)

Deepak Annasaheb Vidhate; Parag Arun Kulkarni

doi:doi:10.11648/j.ijiis.20170606.12

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Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)

Deepak Annasaheb Vidhate, Parag Arun Kulkarni

Published in International Journal of Intelligent Information Systems (Volume 6, Issue 6)

Received: 2 October 2017 Accepted: 17 October 2017 Published: 7 December 2017

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Abstract

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.

Published in	International Journal of Intelligent Information Systems (Volume 6, Issue 6)
DOI	10.11648/j.ijiis.20170606.12
Page(s)	72-84
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Cooperation Schemes, Multi-Agent Learning, Reinforcement Learning

References

[1]	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.
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[3]	Andrew Y. Ng, "Sharding and Policy Search in Reinforcement Learning". Ph.D. dissertation. The University of California, Berkeley, 2003.
[4]	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
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[6]	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.
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[10]	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.
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[12]	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.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	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.
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[19]	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.
[20]	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.
[21]	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.
[22]	Zahra Abbasi, Mohammad Ali Abbasi “Reinforcement Distribution in a Team of Cooperative Q learning Agent”, Proceedings of the 9^th ACIS International Conference on Artificial Intelligence, Distributed Computing, IEEE 2012.
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[24]	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.
[25]	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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APA Style

Deepak Annasaheb Vidhate, Parag Arun Kulkarni. (2017). Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). International Journal of Intelligent Information Systems, 6(6), 72-84. https://doi.org/10.11648/j.ijiis.20170606.12

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ACS Style

Deepak Annasaheb Vidhate; Parag Arun Kulkarni. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). Int. J. Intell. Inf. Syst. 2017, 6(6), 72-84. doi: 10.11648/j.ijiis.20170606.12

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AMA Style

Deepak Annasaheb Vidhate, Parag Arun Kulkarni. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). Int J Intell Inf Syst. 2017;6(6):72-84. doi: 10.11648/j.ijiis.20170606.12

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@article{10.11648/j.ijiis.20170606.12,
  author = {Deepak Annasaheb Vidhate and Parag Arun Kulkarni},
  title = {Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)},
  journal = {International Journal of Intelligent Information Systems},
  volume = {6},
  number = {6},
  pages = {72-84},
  doi = {10.11648/j.ijiis.20170606.12},
  url = {https://doi.org/10.11648/j.ijiis.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20170606.12},
  abstract = {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.},
 year = {2017}
}

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TY  - JOUR
T1  - Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)
AU  - Deepak Annasaheb Vidhate
AU  - Parag Arun Kulkarni
Y1  - 2017/12/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijiis.20170606.12
DO  - 10.11648/j.ijiis.20170606.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 72
EP  - 84
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20170606.12
AB  - 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.
VL  - 6
IS  - 6
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Deepak Annasaheb Vidhate, Parag Arun Kulkarni. (2017). Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). International Journal of Intelligent Information Systems, 6(6), 72-84. https://doi.org/10.11648/j.ijiis.20170606.12

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ACS Style

Deepak Annasaheb Vidhate; Parag Arun Kulkarni. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). Int. J. Intell. Inf. Syst. 2017, 6(6), 72-84. doi: 10.11648/j.ijiis.20170606.12

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AMA Style

Deepak Annasaheb Vidhate, Parag Arun Kulkarni. Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA). Int J Intell Inf Syst. 2017;6(6):72-84. doi: 10.11648/j.ijiis.20170606.12

Copy | Download

@article{10.11648/j.ijiis.20170606.12,
  author = {Deepak Annasaheb Vidhate and Parag Arun Kulkarni},
  title = {Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)},
  journal = {International Journal of Intelligent Information Systems},
  volume = {6},
  number = {6},
  pages = {72-84},
  doi = {10.11648/j.ijiis.20170606.12},
  url = {https://doi.org/10.11648/j.ijiis.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20170606.12},
  abstract = {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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)
AU  - Deepak Annasaheb Vidhate
AU  - Parag Arun Kulkarni
Y1  - 2017/12/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijiis.20170606.12
DO  - 10.11648/j.ijiis.20170606.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 72
EP  - 84
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20170606.12
AB  - 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.
VL  - 6
IS  - 6
ER  -

Copy | Download