Implementation of Multiagent Learning Algorithms for Improved Decision Making

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-35 Number-2
Year of Publication : 2016
Authors : Deepak A. Vidhate, Dr. Parag Kulkarni
  10.14445/22312803/IJCTT-V35P111

MLA

Deepak A. Vidhate, Dr. Parag Kulkarni "Implementation of Multiagent Learning Algorithms for Improved Decision Making". International Journal of Computer Trends and Technology (IJCTT) V35(2):60-66, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The output of the system is a sequence of actions in some applications. There is no such measure as the best action in any in-between state; an action is excellent if it is part of a good policy. A single action is not important; the policy is important that is the sequence of correct actions to reach the goal. In such a case, machine learning program should be able to assess the goodness of policies and learn from past good action sequences to be able to generate a policy. A multi-agent environment is one in which there is more than one agent, where they interact with one another, and further, where there are restrictions on that environment such that agents may not at any given time know everything about the world that other agents know. Two features of multi-agent learning which establish its study as a separate field from ordinary machine learning. Parallelism, scalability, simpler construction and cost effectiveness are main characteristics of multi-agent systems. Multiagent learning model is given in this paper. Two multiagent learning algorithms i. e. Strategy Sharing & Joint Rewards algorithm are implemented. In Strategy Sharing algorithm simple averaging of Q tables is taken. Each Q-learning agent learns from all of its teammates by taking the average of Qtables. Joint reward learning algorithm combines the Q learning with the idea of joint rewards. Paper shows result and performance comparison of the two multiagent learning algorithms.

References
[1] Adnan M. Al-Khatib “Cooperative Machine Learning Method” World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol.1, No.9, 380-383, 2011.
[2] Babak Nadjar Araabi, Sahar Mastoureshgh, and Majid Nili Ahmadabadi “A Study on Expertise of Agents and Its Effects on Cooperative Q-Learning” IEEE Transactions on Evolutionary Computation, vol:14, pp:23-57, 2010
[3] Dr. Hamid R. Berenji David Vengerov “Learning, Cooperation, and Coordination in Multi-Agent Systems”, in Proceedings of 9th IEEE International Conference on Fuzzy Systems, 2000.
[4] Ethem Alpaydin “Introduction to Machine Learning” Second Edition, MIT Press by PHI.
[5] Jun-Yuan Tao, De-Sheng Li “Cooperative Strategy Learning In Multi-Agent Environment With Continuous State Space”, IEEE International Conference on Machine Learning and Cybernetics, pp.2107 – 2111, 2006.
[6] La-mei GAO, Jun ZENG, Jie WU, Min LI “Cooperative Reinforcement Learning Algorithm to Distributed Power System based on Multi-Agent” 2009 3rd International Conference on Power Electronics Systems and Applications Digital Reference: K210509035
[7] Liviu Panait Sean Luke “Cooperative Multi-Agent Learning: The State of the Art”, published in Journal of Autonomous Agents and Multi-Agent Systems Volume 11 Issue 3, pp. 387 – 434, 05.
[8] M.V. Nagendra Prasad & Victor R. Lesser “Learning Situation-Specific Coordination in Cooperative Multiagent Systems” in Journal of Autonomous Agents and Multi-Agent Systems, Volume 2 Issue 2, pp. 173 – 207, 1999.
[9] Michael Kinney & Costas Tsatsoulis “Learning Communication Strategies in Multiagent Systems”, in Journal of Applied Intelligence, Volume 9 Issue 1, pp 71- 91, 1998.
[10] Ronen Brafman & Moshe Tennenholtz “Learning to Coordinate Efficiently: A Model-based Approach”, in Journal of Artificial Intelligence Research, Volume 19 Issue 1, pp. 11-23, 2003.
[11] Tom Mitchell “Machine Learning” McGraw Hill International Edition.

Keywords
Joint Rewards, Multiagent, Q-Learning, Reinforcement Learning, Strategy Sharing.