Policy Blending

Policy blending combines multiple control policies to achieve improved performance or robustness in complex systems. Current research focuses on developing efficient algorithms, such as hierarchical methods and optimal transport frameworks, to blend policies effectively, often leveraging deep reinforcement learning and probabilistic inference. This approach is particularly valuable in robotics and process control, where it addresses challenges like adapting to user preferences, handling large action spaces, and mitigating uncertainties in dynamic environments. The resulting generalized and adaptable policies enhance system performance and safety across diverse applications.