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| Titel: |
An Analysis of Q-Learning Algorithms with Strategies of Reward Function |
| Auteur: |
Ms.S.Manju Dr.Ms.M.Punithavalli |
| Verschenen in: |
International journal on computer science and engineering |
| Paginering: | Jaargang 3 (2011) nr. 2 pagina's 814-820 |
| Jaar: |
2011 |
| Inhoud: |
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 policythereafter. 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 discussesabout the different strategies of Q-Learning algorithms and reward function. |
| Uitgever: |
Engg Journals Publications (provided by DOAJ) |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 14 van 127 gevonden artikelen
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