A parallel implementation of Q-learning based on communication with cache
| Autor: | Alicia Marcela Printista, Marcelo Luis Errecalde, Cecilia Inés Montoya |
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| Jazyk: | angličtina |
| Rok vydání: | 2002 |
| Předmět: | |
| Zdroj: | Journal of Computer Science and Technology, Vol 1, Iss 06, Pp 11 p.-11 p. (2002) |
| Druh dokumentu: | article |
| ISSN: | 1666-6046 1666-6038 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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