A parallel implementation of Q-learning based on communication with cache
- Autores: Alicia Marcela Printista, Marcelo Luis Errecalde, Cecilia Inés Montoya
- Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 1, Nº. 6, 2002 (Ejemplar dedicado a: Sixth Issue; 9 p.)
- Idioma: inglés
- Enlaces
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Resumen
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.
