RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN

no code implementations • 12 Nov 2021 • Peizheng Li, Jonathan Thomas, Xiaoyang Wang, Ahmed Khalil, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Arjun Parekh, Angela Doufexi, Arman Shojaeifard, Robert Piechocki

We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).

Management reinforcement-learning +1

Sim2real for Reinforcement Learning Driven Next Generation Networks

no code implementations • 8 Jun 2022 • Peizheng Li, Jonathan Thomas, Xiaoyang Wang, Hakan Erdol, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Arjun Parekh, Angela Doufexi, Arman Shojaeifard, Robert Piechocki

One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment.

Data Interaction reinforcement-learning +1