Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing
This addresses optimizing resource management for operating systems under dynamic workloads, but it is incremental as it builds on existing meta-RL and world model methods.
The paper tackled load balancing in operating systems by integrating meta-reinforcement learning with DreamerV3, resulting in rapid adaptation to dynamic workloads and outperforming A2C in trials with robust resilience to forgetting.
We integrate a meta-reinforcement learning algorithm with the DreamerV3 architecture to improve load balancing in operating systems. This approach enables rapid adaptation to dynamic workloads with minimal retraining, outperforming the Advantage Actor-Critic (A2C) algorithm in standard and adaptive trials. It demonstrates robust resilience to catastrophic forgetting, maintaining high performance under varying workload distributions and sizes. These findings have important implications for optimizing resource management and performance in modern operating systems. By addressing the challenges posed by dynamic and heterogeneous workloads, our approach advances the adaptability and efficiency of reinforcement learning in real-world system management tasks.