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

• URL: http://arxiv.org/abs/2505.10033v2
• Date: Thu, 05 Jun 2025 15:31:07 GMT
• Title: Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
• Authors: Luis F. W. Batista, Stéphanie Aravecchia, Seth Hutchinson, Cédric Pradalier,
• Abstract summary: We evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations.<n>We train the agent using domain randomization and evaluate its performance in real-world field tests.<n>We provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.
• Score: 13.879128887794462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training [62.536191233049614]
  Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs)<n>This work investigates this problem through extensive experiments on complex card games, such as 24 points, and embodied tasks from ALFWorld.<n>We find that when rewards are based solely on action outcomes, RL fails to incentivize CoT reasoning in VLMs, instead leading to a phenomenon we termed thought collapse.
  arXiv  Detail & Related papers  (2025-03-11T15:17:02Z)
• Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance [0.0]
  Deep Reinforcement Learning (DRL) has emerged as a promising control framework. Current DRL algorithms require disproportionally large computational resources to find near-optimal policies. This paper presents a comprehensive exploration of our proposed approach in maritime control systems.
  arXiv  Detail & Related papers  (2024-03-31T09:25:28Z)
• Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey [8.1138182541639]
  Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments.<n>It remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications.<n>A way to improve robustness of DRL to unknown changes in the environmental conditions and possible perturbations is through Adversarial Training.
  arXiv  Detail & Related papers  (2024-03-01T10:16:46Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Assessing the Impact of Distribution Shift on Reinforcement Learning Performance [0.0]
  Reinforcement learning (RL) faces its own set of unique challenges. Comparison of point estimates, and plots that show successful convergence to the optimal policy during training, may obfuscate overfitting or dependence on the experimental setup. We propose a set of evaluation methods that measure the robustness of RL algorithms under distribution shifts.
  arXiv  Detail & Related papers  (2024-02-05T23:50:55Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations [92.25604137490168]
  This paper introduces a theoretical investigation into efficient reinforcement learning in control systems. We present algorithms and establish near-optimal regret upper and lower bounds, of the form $tildemathcalO(sqrtrm poly(H) SAK)$, for RL in the delayed and missing observation settings.
  arXiv  Detail & Related papers  (2023-06-02T02:46:39Z)
• Testing of Deep Reinforcement Learning Agents with Surrogate Models [10.243488468625786]
  Deep Reinforcement Learning (DRL) has received a lot of attention from the research community in recent years. In this paper, we propose a search-based approach to test such agents.
  arXiv  Detail & Related papers  (2023-05-22T06:21:39Z)
• Sim-Anchored Learning for On-the-Fly Adaptation [45.123633153460034]
  Fine-tuning simulation-trained RL agents with real-world data often degrades crucial behaviors due to limited or skewed data distributions.<n>We propose framing live-adaptation as a multi-objective optimization problem, where policy objectives must be satisfied both in simulation and reality.
  arXiv  Detail & Related papers  (2023-01-17T16:16:53Z)
• Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation [78.17108227614928]
  We propose a benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. We consider a value-based and policy-gradient Deep Reinforcement Learning (DRL) We also propose a verification strategy that checks the behavior of the trained models over a set of desired properties.
  arXiv  Detail & Related papers  (2021-12-16T16:53:56Z)
• Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems [10.499662874457998]
  Black-box nature of Deep Reinforcement Learning (DRL) and uncertain deployment-environments of Robotics pose new challenges on its dependability. In this paper, we define a set of dependability properties in temporal logic and construct a Discrete-Time Markov Chain (DTMC) to model the dynamics of risk/failures of a DRL-driven RAS. Our experimental results show that the proposed method is effective as a holistic assessment framework, while uncovers conflicts between the properties that may need trade-offs in the training.
  arXiv  Detail & Related papers  (2021-09-14T08:42:29Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations [88.94162416324505]
  A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks.
  arXiv  Detail & Related papers  (2020-03-19T17:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.