e-ISSN : 0975-3397
Print ISSN : 2229-5631
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ABSTRACT

Title : An Analysis of Q-Learning Algorithms with Strategies of Reward Function
Authors : Ms.S.Manju, Dr.Ms.M.Punithavalli
Keywords : Machine Learning; Reinforcement Learning; Q-Learning and Relative Q- Learning Methods.
Issue Date : February 2011.
Abstract :
Q-Learning is a Reinforcement Learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-Learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. Reinforcement Learning is an approach where the agent needs no teacher to learn how to solve a problem. The only signal used by the agent to learn from his actions in reinforcement environment is the so called reward, a number which tells the agent if his last action was good (or) not. Q-Learning is a recent form of Reinforcement Learning algorithm that does not need a model of its environment and can be used on-line. This paper discusses about the different strategies of Q-Learning algorithms and reward function.
Page(s) : 814-820
ISSN : 0975–3397
Source : Vol. 3, Issue.02

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