• Meta Reinforcement Learning for Optimization of Electric Circuit Parameters
    The design and optimization of electric circuits is currently still an experience driven approach. Especially in the case of resonant systems, the strong non-linear system behavior requires a lot of optimization loops during the design process to maximize power transfer and efficiency. A simple example of the above systems is the boost converter. Recently, methods using genetic algorithms (GAs) like…
    Continue reading »
  • Multi-Agent Reinforcement Learning for the Coordination of Base Stations in 6G Networks
    6G technology is promising to fundamentally change how consumers and businesses communicate, based on its envisioned speed and flexibility. This flexibility stems from the complex interplay between large-scale ecosystems of software and hardware network components and renders classical theoretical approaches unable to seamlessly scale to the massive problem size [1]. Reinforcement learning [2] can provide a viable approach to alleviate…
    Continue reading »