Application of the LSPI reinforcement learning technique to a co-located network negotiation problem

Optimizing multiple co-located networks, each with a variable number of network functionalities that influence each other, is a complex problem that has not yet received a lot of attention in the research community. However, since independent co-located networks increasingly influence each other, optimization solutions can no longer afford to look only at the performance of a single network. To this end, we propose a multi-tiered solution, based on Least Square Policy Improvement (LSPI), a machine learning technique.