1.
IEEE Trans Syst Man Cybern B Cybern
; 38(4): 924-9, 2008 Aug.
Artigo
em Inglês
| MEDLINE
| ID: mdl-18632379
RESUMO
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on finding steady-state policies for deterministic time-invariant discrete time control problems with continuous states and actions often found in robotics. In this paper, we describe our approach and provide initial results on several simulated robotics problems.