  The optimal placement of sensors for environmental monitoring and disaster
management is a challenging problem due to its NP-hard nature. Traditional
methods for sensor placement involve exact, approximation, or heuristic
approaches, with the latter being the most widely used. However, heuristic
methods are limited by expert intuition and experience. Deep learning (DL) has
emerged as a promising approach for generating heuristic algorithms
automatically. In this paper, we introduce a novel sensor placement approach
focused on learning improvement heuristics using deep reinforcement learning
(RL) methods. Our approach leverages an RL formulation for learning improvement
heuristics, driven by an actor-critic algorithm for training the policy
network. We compare our method with several state-of-the-art approaches by
conducting comprehensive experiments, demonstrating the effectiveness and
superiority of our proposed approach in producing high-quality solutions. Our
work presents a promising direction for applying advanced DL and RL techniques
to challenging climate sensor placement problems.
