用视觉导航无人机沿河飞行，安全强化学习表现更优。
Vision-driven UAV River Following: Benchmarking with Safe Reinforcement Learning
- 用语义增强图像编码压缩状态表示，提升训练效率。
- 一阶策略约束优化算法在奖赏与安全间平衡最佳。
- 在线算法优于离线和基于模型的方法，适用水面航行器。
本研究在基于Unity的逼真仿真环境中，全面评估了安全强化学习（Safe RL）算法在无人机视觉驱动河流跟随任务中的表现。通过相对熵与水体像素重建质量验证了语义增强图像编码的有效性。基于重建损失确定编码维度，实现更紧凑的状态表示，促进安全策略训练。所有测试算法中，策略空间的一阶约束优化在奖励获取与安全合规间达到最佳平衡。值得注意的是，在训练与测试环境中，在线算法始终优于离线及基于模型的算法。该基准结果与视觉编码方法可推广至受限水域自主航行的水面无人艇（ASV）。
原文摘要 · Abstract (English)
In this study, we conduct a comprehensive benchmark of the Safe Reinforcement Learning (Safe RL) algorithms for the task of vision-driven river following of Unmanned Aerial Vehicle (UAV) in a Unity-based photo-realistic simulation environment. We empirically validate the effectiveness of semantic-augmented image encoding method, assessing its superiority based on Relative Entropy and the quality of water pixel reconstruction. The determination of the encoding dimension, guided by reconstruction loss, contributes to a more compact state representation, facilitating the training of Safe RL policies. Across all benchmarked Safe RL algorithms, we find that First Order Constrained Optimization in Policy Space achieves the optimal balance between reward acquisition and safety compliance. Notably, our results reveal that on-policy algorithms consistently outperform both off-policy and model-based counterparts in both training and testing environments. Importantly, the benchmarking outcomes and the vision encoding methodology extend beyond UAVs, and are applicable to Autonomous Surface Vehicles (ASVs) engaged in autonomous navigation in confined waters.
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