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COLREGs-compliant multiship collision avoidance based on deep reinforcement learning - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This article proposes a robust and efficient approach to the system for multiple ships based on the Deep Reinforcement Learning (DRL) algorithm.
2. The proposed system is compliant with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs).
3. The proposed system can handle complex scenarios with multiple target ships, while considering environmental disturbances and operational constraints.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed overview of the proposed collision avoidance system based on deep reinforcement learning, which is compliant with COLREGs. The authors provide evidence for their claims by citing relevant research studies in the literature review section, which adds credibility to their argument. Furthermore, they provide a comprehensive description of the proposed system and its potential applications in congested water areas such as Amsterdam.
However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, while the authors mention that more than 80% of marine collision accidents are caused by or related to human decision failures concerning the lack of situational awareness and failure to comply with COLREGs, they do not provide any evidence or data to support this claim. Additionally, while they discuss potential risks associated with implementing such a system, they do not explore counterarguments or alternative solutions that could be used instead of deep reinforcement learning-based systems. Finally, while they cite several relevant studies in their literature review section, they do not present both sides equally; rather, they focus mainly on studies that support their argument without exploring other possible approaches or solutions.