International Journal of Computer
Trends and Technology

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Volume 35 | Number 1 | Year 2016 | Article Id. IJCTT-V35P111 | DOI : https://doi.org/10.14445/22312803/IJCTT-V35P111

Implementation of Multiagent Learning Algorithms for Improved Decision Making

Deepak A. Vidhate, Dr. Parag Kulkarni

Citation :

Deepak A. Vidhate, Dr. Parag Kulkarni, "Implementation of Multiagent Learning Algorithms for Improved Decision Making," International Journal of Computer Trends and Technology (IJCTT), vol. 35, no. 1, pp. 60-66, 2016. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V35P111

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.

Keywords

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

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