id: 06069355
dt: a
an: 06069355
au: Tziortziotis, Nikolaos; Blekas, Konstantinos
ti: Value function approximation through sparse Bayesian modeling.
so: Sanner, Scott (ed.) et al., Recent advances in reinforcement learning. 9th
    European workshop, EWRL 2011, Athens, Greece, September 9‒11, 2011.
    Revised selected papers. Berlin: Springer (ISBN 978-3-642-29945-2/pbk).
    Lecture Notes in Computer Science 7188. Lecture Notes in Artificial
    Intelligence, 128-139 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: value function approximation; sparse Bayesian modeling; relevance vector
    machine; incremental learning
ci: 
li: doi:10.1007/978-3-642-29946-9_15
ab: Summary: In this study we present a sparse Bayesian framework for value
    function approximation. The proposed method is based on the on-line
    construction of a dictionary of states which are collected during the
    exploration of the environment by the agent. A linear regression model
    is established for the observed partial discounted return of such
    dictionary states, where we employ the Relevance Vector Machine (RVM)
    and exploit its enhanced modeling capability due to the embedded
    sparsity properties. In order to speed-up the optimization procedure
    and allow dealing with large-scale problems, an incremental strategy is
    adopted. A number of experiments have been conducted on both simulated
    and real environments, where we took promising results in comparison
    with another Bayesian approach that uses Gaussian processes.
rv: