Residual Policy

Residual policy learning focuses on improving existing policies, rather than training entirely new ones from scratch, offering efficiency gains in various applications. Current research explores its use across diverse fields, including robotics (e.g., grasping, assembly), scheduling optimization, and traffic control, often employing reinforcement learning algorithms with architectures like actor-critic networks and graph neural networks to refine pre-trained models. This approach demonstrates significant potential for accelerating training times, enhancing performance in complex tasks, and adapting to individual needs or varying environmental conditions, leading to more efficient and robust solutions in diverse domains.