Projective simulation for classical learning agents: a comprehensive investigation
| Autor: | Mautner, Julian, Manzano Diosdado, Daniel |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
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| Popis: | We study the model of projective simulation (PS), a novel approach to arti cial intelligence based on stochastic processing of episodic memory which was recently introduced [1]. Here we provide a detailed analysis of the model and examine its performance, including its achievable e ciency, its learning times and the way both properties scale with the problems' dimension. In addition, we situate the PS agent in di erent learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model's exibility. Further more, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classi er systems, two popular models in the eld of reinforcement learning. It is shown that PS is a competitive arti cial intelligence model of unique properties and strengths. Austrian Science Fund (FWF) SFB FoQuS F4012 Templeton World Charity Foundation (TWCF) |
| Databáze: | OpenAIRE |
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