Deep Reinforcement Learning Based Placement

Deep reinforcement learning (DRL) is increasingly used to optimize the placement of computational tasks and resources across diverse systems, aiming to minimize latency, cost, and energy consumption while maximizing performance. Current research focuses on applying DRL to various placement problems, including container image distribution in cloud-edge computing, UAV placement in 5G networks, and optimizing the placement of neural network components across multiple devices. These advancements are significant for improving efficiency in distributed computing, enabling adaptive resource management in dynamic environments, and optimizing the design of complex systems like neuromorphic processors.