Answer: Q-learning, Deep Q-Network (DQN)

Q-learning and Deep Q-Network (DQN) are both Reinforcement Learning (RL) algorithms suitable for this scenario as they evaluate potential solutions through a system of rewards over repeated attempts without the need for labeled data. Q-learning is a model-free RL algorithm that can handle problems with stochastic transitions and rewards without requiring a model of the environment. DQN extends Q-learning with deep learning, enabling it to learn successful policies directly from high-dimensional sensory inputs. Both are ideal for dynamically determining the optimal placement of nodes in a cost-effective and scalable manner. - **Option A (K-Nearest Neighbors)** is incorrect because it's a supervised classification algorithm that relies on labeled data for new classifications based on the closest known examples. - **Option C (Support Vector Machine)** is also incorrect as it's a supervised ML algorithm that involves distances computed with a hyper-plane to divide classifications, not between data points. - **Option D (K-means)** is unsuitable because, although it's an unsupervised learning algorithm for clustering, it's not designed for the scope of evaluating solutions through rewards like Q-learning or DQN.

Author: LeetQuiz Editorial Team