Please use this identifier to cite or link to this item:
|Title:||Sampling-based algorithms for continuous-time POMDPs|
|Source:||Chaudhari, P.,Karaman, S.,Hsu, D.,Frazzoli, E. (2013). Sampling-based algorithms for continuous-time POMDPs. Proceedings of the American Control Conference : 4604-4610. ScholarBank@NUS Repository.|
|Abstract:||This paper focuses on a continuous-time, continuous-space formulation of the stochastic optimal control problem with nonlinear dynamics and observation noise. We lay the mathematical foundations to construct, via incremental sampling, an approximating sequence of discrete-time finite-state partially observable Markov decision processes (POMDPs), such that the behavior of successive approximations converges to the behavior of the original continuous system in an appropriate sense. We also show that the optimal cost function and control policies for these POMDP approximations converge almost surely to their counterparts for the underlying continuous system in the limit. We demonstrate this approach on two popular continuous-time problems, viz., the Linear-Quadratic-Gaussian (LQG) control problem and the light-dark domain problem. © 2013 AACC American Automatic Control Council.|
|Source Title:||Proceedings of the American Control Conference|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 14, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.