Enhancement in Decision Making with Improved Performance by Multiagent Learning Algorithms
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 3
Abstract
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 notimportant; 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 actionsequences 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 Q-tables. 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.
Authors and Affiliations
Deepak A. Vidhate , Dr. Parag Kulkarni
Keywords
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