Statistical seminar

Abstract: We study the use of multi-agent reinforcement learning (MARL) for buffered cellular networks, where base stations are modelled as independent agents making transmission decisions under interference and delay constraints. The network is described through a stochastic geometry framework with Poisson-distributed base stations and users, and buffers capturing traffic arrivals and service dynamics. To handle the interactions between agents, we employ a mean-field approximation, so that each agent responds to an aggregate distribution of its neighbours’ states and actions. The learning problem is formulated via mean-field Q-learning, where the objective is to improve network capacity while controlling delays. Initial experiments show convergence of the Q-functions for several agents, suggesting that the approach is well-suited to this setting.