测试强化学习控制船在真实环境下的抗干扰能力
Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
- 用领域随机化训练强化学习模型，提升泛化性
- 真实海况下仍能稳定抓取漂浮垃圾，性能下降小于15%
- 适合关注海上机器人部署的工程师和研究者
尽管深度强化学习（DRL）在自主水面航行器（ASVs）控制中取得显著进展，但其在真实环境、特别是外部扰动下的鲁棒性仍缺乏充分探索。本文评估了一个用于捕获漂浮垃圾的DRL代理在多种扰动下的韧性。通过领域随机化训练代理，并在真实场景中进行测试，评估其应对不对称阻力和偏心载荷等意外扰动的能力。对比仿真与实测结果，量化性能退化，并与模型预测控制（MPC）基线进行比较。结果显示，该DRL代理在显著扰动下仍表现可靠。我们开源了实现代码，提供有效训练策略、实际部署挑战及实用建议。
原文摘要 · Abstract (English)
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.
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