Two Timescale

Two-timescale methods address problems involving processes operating at significantly different speeds, enabling efficient optimization and control. Current research focuses on applying these methods within reinforcement learning, particularly using algorithms like Q-learning and its variants, often coupled with function approximation or deep learning architectures to solve complex problems in areas such as multi-agent systems and resource allocation in edge computing. This approach offers improved performance and efficiency in various applications, including digital twin synchronization, mean field control games, and resource management in collaborative edge computing systems, by effectively handling the interplay between fast and slow dynamics.