Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning
Mohammed Abouheaf, Shuzheng Qu, Wail Gueaieb, Rami Abielmona, and Moufid Harb

TL;DR
This paper proposes a reinforcement learning approach for maritime law enforcement vessels to intercept illegal fishing ships, aiming to combat IUU fishing and its environmental and economic impacts.
Contribution
It introduces a novel ML framework using online reinforcement learning for real-time interception of illegal vessels in maritime domains.
Findings
Effective interception strategies generated in real-time
Model-free approach adapts to dynamic maritime scenarios
Potential to enhance maritime security and resource protection
Abstract
This article elaborates on how machine learning (ML) can leverage the solution of a contemporary problem related to the security of maritime domains. The worldwide ``Illegal, Unreported, and Unregulated'' (IUU) fishing incidents have led to serious environmental and economic consequences which involve drastic changes in our ecosystems in addition to financial losses caused by the depletion of natural resources. The Fisheries and Aquatic Department (FAD) of the United Nation's Food and Agriculture Organization (FAO) issued a report which indicated that the annual losses due to IUU fishing reached $25 Billion. This imposes negative impacts on the future-biodiversity of the marine ecosystem and domestic Gross National Product (GNP). Hence, robust interception mechanisms are increasingly needed for detecting and pursuing the unrelenting illegal fishing incidents in maritime territories.…
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