An Incremental Sampling-based Algorithm for Stochastic Optimal Control

Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation methods and sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process (iMDP) to compute incrementally control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the origin...

• Type: Journal
• Year: 2012
• Where: CORR
• Authors: Vu Anh Huynh, Sertac Karaman, Emilio Frazzoli
• Keywords: CORR 2012, Education, Markov Decision Process, Optimal Control Problem, Optimal Value Function