A parallel implementation of Q-learning based on communication with cache
Q-Learning is a Reinforcement Learning method for solving sequential decision problems, where the utility of actions depends on a sequence of decisions and there exists uncertainty about the dynamics of the environment the agent is situated on. This general framework has allowed that Q-Learning and...
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| Autores principales: | , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2002
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9432 http://journal.info.unlp.edu.ar/wp-content/uploads/p41.pdf |
| Aporte de: |
| Sumario: | Q-Learning is a Reinforcement Learning method for solving sequential decision problems, where the utility of actions depends on a sequence of decisions and there exists uncertainty about the dynamics of the environment the agent is situated on. This general framework has allowed that Q-Learning and other Reinforcement Learning methods to be applied to a broad spectrum of complex real world problems such as robotics, industrial manufacturing, games and others. Despite its interesting properties, Q-learning is a very slow method that requires a long period of training for learning an acceptable policy. In order to solve or at least reduce this problem, we propose a parallel implementation model of Q-learning using a tabular representation and via a communication scheme based on cache. This model is applied to a particular problem and the results obtained with different processor configurations are reported. A brief discussion about the properties and current limitations of our approach is finally presented. |
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