This chapter explores combinatorial optimization techniques for sensor placement within the context of applications for gravitational wave detectors. In cases where an objective function, being optimised subject to a cardinality constraint, is submodular, or has a diminishing returns property, and is monotone, a simple greedy algorithm achieves near-optimal performance. More broadly, in the context of gravitational wave interferometry, placing sensors requires the optimisation of objective functions that are neither submodular nor supermodular. Such NP-hard optimisation problems can be addressed through the use of a priori hand-crafted heuristics and meta-heuristics inspired by natural, biological, and evolutionary processes. This chapter introduces a novel approach to address the problem by learning latent functions using pointer networks within an actor-critic framework, leveraging attention networks along with deep reinforcement learning. Preliminary implementation challenges and future directions are discussed, highlighting the potential for improved scalability and efficiency for complex optimization tasks.