RMRL: improved regret minimisation techniques using learning automata-Reference-Cited by-同舟云学术

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RMRL: improved regret minimisation techniques using learning automata

Published:2018-12-15 Issue:3 Volume:31 Page:441-454
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Ghasemi Safiye¹,Meybodi Mohammad Reza²,Takht-Fooladi Mehdi Dehghan²,Rahmani Amir Masoud³⁴

Affiliation:

1. Department of Engineering, Sepidan Branch, Islamic Azad University, Sepidan, Iran

2. Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

3. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

4. Computer Science, University of Human Development, Sulaimanyah, Iraq

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2018.1554711

Reference19 articles.

1. Strategic best-response learning in multiagent systems

2. The Orgins and Evolution of Predator-Prey Theory

3. Modified predator-prey algorithm for constrained and unconstrained multi-objective optimisation

4. Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

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