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.
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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
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DOI: https://doi.org/10.1007/978-981-10-4603-2_15
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