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Title:
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REINFORCEMENT LEARNING ACTOR-CRITIC MATHEMATICS MODEL |
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Author(s):
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Samia L. Jones |
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ISBN:
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978-972-8924-97-3 |
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Editors:
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Hans Weghorn and Pedro Isaías |
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Year:
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2009 |
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Edition:
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V II, 2 |
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Keywords:
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Reinforcement Learning, Actor-Critic |
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Type:
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Short Paper |
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First Page:
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44 |
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Last Page:
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48 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Reinforcement learning is the strategy that uses an agent that must learn behavior through trial-and-error interactions with
a dynamic environment. There are two main strategies for solving reinforcement-learning problems. The first is to search
in the space of behaviors in order to find one that performs well in the environment. The second is to use statistical
techniques and dynamic programming methods to estimate the utility of taking actions in states of the world.
This paper presents a learning system simulator that without prior knowledge about the user in advanced can help to train
users to solve Mathematics problems and give rewards. This system will use the Actor-Critic method. |
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