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

arXiv: https://arxiv.org/abs/2505.10033 · HTML: https://mlsanity.com/paper/2505.10033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 15 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles](https://mlsanity.com/paper/2008.07240/) (2020)
- [Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment](https://mlsanity.com/paper/2210.08487/) (2022)
- [Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning](https://mlsanity.com/paper/2307.16240/) (2023)
- [Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters](https://mlsanity.com/paper/2302.08100/) (2023)
- [RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts](https://mlsanity.com/paper/2310.07393/) (2023)
- [A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation](https://mlsanity.com/paper/2407.08263/) (2024)
- [OtterROS: Picking and Programming an Uncrewed Surface Vessel for Experimental Field Robotics Research with ROS 2](https://mlsanity.com/paper/2404.05627/) (2024)

## Built on by (most cited first, 2 of 2)

- [Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture](https://mlsanity.com/paper/2605.02529/) (2026)
- [Sim-to-Real RL for ASVs using SysID](https://mlsanity.com/paper/2610.12202/) (2026)

## Abstract (arXiv)

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.