Abstract
A policy framework for dynamic products availability in three retailer shops in the market is investigated. Retailers can cooperate with each other and can get benefit from cooperative information by their own policies that accurately represent their goals and interests. The retailers are the learning agents in the system and use reinforcement learning to learn cooperatively from the situation. Cooperation in learning (CL) is understood in a multi-agent environment. A framework for Improved Cooperative Learning Algorithms with Expertness (ICLAE) is proposed. Expertness measuring criteria which were used in earlier work are further enhanced and improved in the proposed method. Four methods for measuring the agents’ expertness are used, viz., Normal, Absolute, Positive, and Negative. The novelty of this approach lies in the implementation of the RL algorithms with expertness measuring criteria by means of Q-learning, Q(λ) learning, Sarsa learning, and Sarsa(λ) learning algorithms. This chapter shows the implementation results and performance comparison of these algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Araabi, B.N., Mastoureshgh, S., Ahmadabadi, M.N.: A study on expertise of agents and its effects on cooperative Q-learning. IEEE Trans. Evol. Comput. 14, 23–57 (2010)
Vidhate, D.A., Kulkarni, P.: New approach for advanced cooperative learning algorithms using RL methods (ACLA). In: Proceedings of the Third International Symposium on Computer Vision and the Internet (Vision Net’16), pp. 12–20. ACM, DL (2016)
Choi, Y.-C., Student Member, Ahn, H.-S.: A survey on multi-agent reinforcement learning: coordination problems. In: IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, pp. 81–86 (2010)
Abbasi, Z., Abbasi, M.A.: Reinforcement distribution in a team of cooperative Q-learning agent. In: Proceedings of the 9th ACIS International Conference on Software Engineering, Artificial Intelligence, and Parallel/Distributed Computing 978-0-7695-3263-9/08, pp. 154–160. IEEE (2008)
Gao, L.-M., Zeng, J., Wu, J., Li, M.: Cooperative reinforcement learning algorithm to distributed power system based on multi-agent. In: 3rd International Conference on Power Electronics Systems and Applications (Reference: K210509035) (2009)
Al-Khatib, A.M.: Cooperative machine learning method. World Comput. Sci. Inf. Technol. J. 1, 380–383 (2011)
Vidhate, D.A., Kulkarni, P.: Enhancement in decision making with improved performance by multiagent learning algorithms. IOSR J. Comput. Eng. 1(18), 18–25 (2016)
Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. J. Auton. Agents Multi-Agent Syst. 11, 387–434 (2005)
Vidhate, D.A., Kulkarni, P.: Implementation of multiagent learning algorithms for improved decision making. Int. J. Comput. Trends Technol. (IJCTT) 35(2) (2016)
Tao, J.-Y., Li, D.-S.: Cooperative strategy learning in multi-agent environment with continuous state space. In: IEEE International Conference on Machine Learning (2006)
Dr. Berenji, H.R., Vengerov, D.: Learning, Cooperation, and Coordination in Multi-Agent Systems. Intelligent Inference Systems Corp., Technical Report, October 2000
Nagendra Prasad, M.V., Lesser, V.R.: Learning situation-specific coordination in cooperative multi-agent systems. J. Auton. Agents Multi-Agent Syst. 2(2), 173–207 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vidhate, D.A., Kulkarni, P. (2018). A Framework for Improved Cooperative Learning Algorithms with Expertness (ICLAE). In: Choudhary, R., Mandal, J., Bhattacharyya, D. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 562. Springer, Singapore. https://doi.org/10.1007/978-981-10-4603-2_15
Download citation
DOI: https://doi.org/10.1007/978-981-10-4603-2_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4602-5
Online ISBN: 978-981-10-4603-2
eBook Packages: EngineeringEngineering (R0)