TY  - CONF
ID  - upm28942
UR  - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6811729&tag=1
A1  - Sánchez Fernández, Matilde
A1  - Valcarcel Macua, Sergio
A1  - Zazo Bello, Santiago
Y1  - 2013///
N2  - This paper contributes with a unified formulation that merges previ- ous 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 approxi- mated, 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 pro- posed algorithm is illustrated by simulation, showing competitive results when compared with the state-of-the-art solutions.
PB  - IEEE
KW  - Approximate dynamic programming
KW  -  Linear value function approximation
KW  -  Mean squared Bellman Error
KW  -  Mean squared projected Bellman Error
KW  -  Reinforcement Learning
TI  - A unified framework for linear function approximation of value functions in stochastic control
SP  - 1
M2  - Marrakech, Morocco
AV  - public
EP  - 5
T2  - 21st European Signal Processing Conference (EUSIPCO)
ER  -