Path Planning Using Wassertein Distributionally Robust Deep Q-learning

TL;DR

This paper introduces a novel Wasserstein distributionally robust deep Q-learning method for risk-averse robot path planning, effectively balancing safety and goal achievement under uncertainty.

Contribution

It proposes a new risk-averse control framework using Wasserstein distributionally robust deep Q-learning for safe robot navigation.

Findings

The method successfully avoids all obstacles in simulations.

It effectively manages noise uncertainty in path planning.

The approach improves safety compared to traditional methods.

Abstract

We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and propose a continuous reward function design that explicitly takes into account the risk of collision with obstacles while encouraging the robot's motion towards the goal. We learn the risk-averse robot control actions through Lipschitz approximated Wasserstein distributionally robust deep Q-learning to hedge against the noise uncertainty. The learned control actions result in a safe and risk averse trajectory from the source to the goal, avoiding all the obstacles. Various supporting numerical simulations are presented to demonstrate our proposed approach.