The ocean has been investigated for centuries across the world, and planning the travel path for vessels in the ocean has become a hot topic in recent decades as the increasing development of worldwide business trading. Planning such suitable paths is often based on big data processing in cybernetics, while not many investigations have been done. We attempt to find the optimal path for vessels in the ocean by proposing an online learning dispatch approach on studying the mission_executing_feedback (MEF) model. The proposed approach explores the ocean subdomain (OS) to achieve the largest average traveling feedback for different vessels. It balances the ocean path by a deep and wide search, and considers adaptation for these vessels. Further, we propose a contextual multiarmed bandit-based algorithm, which provides accurate exploration results with sublinear regret and significantly improves the learning speed. The experimental results show that the proposed MEF approach possesses 90% accuracy gain over random exploration and achieves about 25% accuracy improvement over other contextual bandit models on supporting big data online learning pre-eminently.

中文翻译：

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大规模海上路径规划：一种情境在线学习方法


世界各地对海洋的研究已有数百年历史，随着全球商业贸易的日益发展，规划海洋中船只的航行路径已成为近几十年来的热门话题。规划这种合适的路径通常基于控制论中的大数据处理，但目前的研究并不多。我们试图通过提出一种在线学习调度方法来研究任务执行反馈（MEF）模型，从而找到海洋中船只的最佳路径。所提出的方法探索海洋子域（OS）以实现不同船只的最大平均行驶反馈。它通过深入而广泛的搜索来平衡海洋路径，并考虑对这些船只的适应。此外，我们提出了一种基于上下文多臂老虎机的算法，它提供了具有次线性遗憾的准确探索结果，并显着提高了学习速度。实验结果表明，所提出的 MEF 方法比随机探索的精度提高了 90%，并且在支持大数据在线学习方面比其他上下文老虎模型提高了约 25% 的精度。