An Analysis of Q-Learning Algorithms with Strategies of Reward Function

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An Analysis of Q-Learning Algorithms with Strategies of Reward Function

Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 2

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.

Authors and Affiliations

Ms. S. Manju, , Dr. Ms. M. Punithavalli,

Keywords

machine learning; Reinforcement Learning; Q-Learning and Relative Q- Learning Methods.

EP ID EP102749
DOI -
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