Performance Function

Performance functions quantify the effectiveness of a system, guiding optimization towards desired outcomes. Current research focuses on developing robust and efficient methods for optimizing these functions, particularly in complex scenarios with uncertainty, such as autonomous systems and multi-agent environments. This involves employing techniques like active learning, adaptive sampling, and distributed policy gradient methods, often coupled with novel algorithms to handle constraints and non-convexities. Improved performance function optimization has significant implications for engineering design, autonomous systems, and multi-agent reinforcement learning, enabling more reliable and efficient systems across diverse applications.