Least Squares Temporal Difference Actor-Critic Methods with Applications to Robot Motion Control

TL;DR

This paper introduces a novel actor-critic algorithm using least squares temporal difference learning for solving probabilistic control problems in robotics, specifically for maximizing reachability while avoiding certain states.

Contribution

It transforms a probabilistic robot motion control problem into a stochastic shortest path problem and develops a new approximate dynamic programming method with proven convergence.

Findings

Algorithm effectively finds policies in simulated robot scenarios.

Convergence to stationary points demonstrated.

Simulation confirms practical applicability.

Abstract

We consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by applications in robotics, where such problems naturally arise when probabilistic models of robot motion are required to satisfy temporal logic task specifications. We transform this problem into a Stochastic Shortest Path (SSP) problem and develop a new approximate dynamic programming algorithm to solve it. This algorithm is of the actor-critic type and uses a least-square temporal difference learning method. It operates on sample paths of the system and optimizes the policy within a pre-specified class parameterized by a parsimonious set of parameters. We show its convergence to a policy corresponding to a stationary point in the parameters' space. Simulation results confirm the effectiveness of the proposed solution.