  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. This article addresses the
problem of coordinating the motion of a fleet of marine vessels (pursuers) to
catch an IUU vessel while still in local waters. The problem is formulated as a
pursuer-evader problem that is tackled within an ML framework. One or more
pursuers, such as law enforcement vessels, intercept an evader (i.e., the
illegal fishing ship) using an online reinforcement learning mechanism that is
based on a value iteration process. It employs real-time navigation
measurements of the evader ship as well as those of the pursuing vessels and
returns back model-free interception strategies.
