2019-12-08T00:49:04Z
https://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/96699
2019-06-10T14:44:59Z
com_10261_106
com_10261_4
col_10261_1241
00925njm 22002777a 4500
dc
Agostini, Alejandro
author
Celaya, Enric
author
2011
In this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one defined in a different region of the domain. Associated with each approximator is a relevance function that locally quantifies the quality of its approximation, so that, at each input point, the approximator with highest relevance can be selected. The relevance function is defined using parametric estimations of the variance of the q-values and the density of samples in the input space, which are used to quantify the accuracy and the confidence in the approximation, respectively. These parametric estimations are obtained from a probability density distribution represented as a Gaussian Mixture Model embedded in the input-output space of each approximator. In our experiments, the proposed approach required a lesser number of experiences for learning and produced more stable convergence profiles than when using a single function approximator.
International Joint Conference on Artificial Intelligence 2: 1146-1151 (2011)
http://hdl.handle.net/10261/96699
A competitive strategy for function approximation in Q-learning