Multi Task Policy

Multi-task policy learning aims to train single agents capable of performing diverse tasks, improving efficiency and adaptability compared to training separate agents for each task. Current research focuses on developing efficient model architectures, such as transformers and diffusion models, and algorithms that effectively leverage limited data, often through active learning strategies or by integrating foresight and multi-modal inputs. This field is significant because it promises more robust and generalizable AI agents for applications in robotics, recommendation systems, and other areas requiring flexible decision-making across multiple objectives or environments.