Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

TL;DR

This paper assesses the robustness of a DRL-based control system for autonomous surface vehicles in real-world disturbances, demonstrating reliable performance and providing practical deployment insights.

Contribution

It evaluates DRL resilience in real-world ASV control under disturbances, using domain randomization and benchmarking against MPC, with open-source implementation.

Findings

DRL agent maintains performance despite disturbances

Benchmarking shows competitive results against MPC

Provides practical training and deployment insights

Abstract

Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected disturbances such as asymmetric drag and an off-center payload. We assess the agent's performance under these perturbations in both simulation and real-world experiments, quantifying performance degradation and benchmarking it against an MPC baseline. Results indicate that the DRL agent performs reliably despite significant disturbances. Along with the open-source release of our implementation, we provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.