Sánchez-Fernández, MatildeValcárcel, SergioZazo, Santiago2015-06-262015-06-262013-09Proceedings of the 21st European Signal Processing Conference (EUSIPCO) (2013) pp. 1-5https://hdl.handle.net/10016/21205The proceeding at:21st European Signal Processing Conference (EUSIPCO 2013), took place 2013, September 9-13, in Marrakech (marroc).This paper contributes with a unified formulation that merges previous analysis on the prediction of the performance (value function) of certain sequence of actions (policy) when an agent operates a Markov decision process with large state-space. When the states are represented by features and the value function is linearly approximated, our analysis reveals a new relationship between two common cost functions used to obtain the optimal approximation. In addition, this analysis allows us to propose an efficient adaptive algorithm that provides an unbiased linear estimate. The performance of the proposed algorithm is illustrated by simulation, showing competitive results when compared with the state-of-the-art solutions.5application/pdfeng© 2013 IEEEFunction approximationMarkov processesSignal processingStochastic systemsconference paperTelecomunicacionesopen access15Proceedings of the 21st European Signal Processing Conference (EUSIPCO)CC/0000022069