A novel approach by Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) for dynamic decision-making in the retail application is proposed in this paper. Performance evaluation between Cooperative Reinforcement Learning Algorithms and Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) is demonstrated. Different cooperation schemes for multi-agent cooperative reinforcement learning i.e. EQ learning, EGroup scheme, EDynamic scheme and EGoal driven scheme are proposed here. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieve excellent action policies. This approach is developed for three retailer stores in the retail marketplace. Retailers are able to help with each other and can obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train helpfully in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is modeled as Markov decision process model that makes it possible to design learning algorithms. Dynamic consumer performance is noticeably learned using the proposed algorithms. 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 expertise based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.
| Published in | Machine Learning Research (Volume 2, Issue 4) |
| DOI | 10.11648/j.mlr.20170204.14 |
| Page(s) | 133-147 |
| 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 |
Cooperation Schemes, Multi-Agent Learning, Reinforcement Learning
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APA Style
Deepak Annasaheb Vidhate, Parag Arun Kulkarni. (2017). Performance Evaluation of Cooperative RL Algorithms for Dynamic Decision Making in Retail Shop Application. Machine Learning Research, 2(4), 133-147. https://doi.org/10.11648/j.mlr.20170204.14
ACS Style
Deepak Annasaheb Vidhate; Parag Arun Kulkarni. Performance Evaluation of Cooperative RL Algorithms for Dynamic Decision Making in Retail Shop Application. Mach. Learn. Res. 2017, 2(4), 133-147. doi: 10.11648/j.mlr.20170204.14
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Download@article{10.11648/j.mlr.20170204.14,
author = {Deepak Annasaheb Vidhate and Parag Arun Kulkarni},
title = {Performance Evaluation of Cooperative RL Algorithms for Dynamic Decision Making in Retail Shop Application},
journal = {Machine Learning Research},
volume = {2},
number = {4},
pages = {133-147},
doi = {10.11648/j.mlr.20170204.14},
url = {https://doi.org/10.11648/j.mlr.20170204.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170204.14},
abstract = {A novel approach by Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) for dynamic decision-making in the retail application is proposed in this paper. Performance evaluation between Cooperative Reinforcement Learning Algorithms and Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) is demonstrated. Different cooperation schemes for multi-agent cooperative reinforcement learning i.e. EQ learning, EGroup scheme, EDynamic scheme and EGoal driven scheme are proposed here. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieve excellent action policies. This approach is developed for three retailer stores in the retail marketplace. Retailers are able to help with each other and can obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train helpfully in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is modeled as Markov decision process model that makes it possible to design learning algorithms. Dynamic consumer performance is noticeably learned using the proposed algorithms. 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 expertise based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.},
year = {2017}
}
TY - JOUR T1 - Performance Evaluation of Cooperative RL Algorithms for Dynamic Decision Making in Retail Shop Application AU - Deepak Annasaheb Vidhate AU - Parag Arun Kulkarni Y1 - 2017/12/12 PY - 2017 N1 - https://doi.org/10.11648/j.mlr.20170204.14 DO - 10.11648/j.mlr.20170204.14 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 133 EP - 147 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20170204.14 AB - A novel approach by Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) for dynamic decision-making in the retail application is proposed in this paper. Performance evaluation between Cooperative Reinforcement Learning Algorithms and Expertise based Multi-agent Cooperative Reinforcement Learning Algorithms (EMCRLA) is demonstrated. Different cooperation schemes for multi-agent cooperative reinforcement learning i.e. EQ learning, EGroup scheme, EDynamic scheme and EGoal driven scheme are proposed here. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieve excellent action policies. This approach is developed for three retailer stores in the retail marketplace. Retailers are able to help with each other and can obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train helpfully in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is modeled as Markov decision process model that makes it possible to design learning algorithms. Dynamic consumer performance is noticeably learned using the proposed algorithms. 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 expertise based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment. VL - 2 IS - 4 ER -
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