Implementation of Multiagent Learning Algorithms for Improved Decision Making
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2016 by IJCTT Journal | ||
| Volume-35 Number-2 |
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| Year of Publication : 2016 | ||
| Authors : Deepak A. Vidhate, Dr. Parag Kulkarni | ||
| DOI : 10.14445/22312803/IJCTT-V35P111 |
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.
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Keywords
Joint Rewards, Multiagent,
Q-Learning, Reinforcement Learning, Strategy
Sharing.