An Incremental Sampling-based Algorithm for Stochastic Optimal Control

TL;DR

This paper introduces an incremental sampling-based algorithm called iMDP for solving continuous-time, continuous-space stochastic optimal control problems, providing a convergent and efficient way to approximate optimal policies.

Contribution

The paper proposes the iMDP algorithm that incrementally refines control policies through random sampling, ensuring convergence to the optimal solution in stochastic control problems.

Findings

The iMDP algorithm converges to the optimal value function with probability one.

It provides an anytime method for computing control policies.

Effective in motion planning in cluttered environments with noise.

Abstract

In this paper, we consider a class of continuous-time, 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 original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise.