Research on Power Control Algorithm Based on Distributed Reinforcement Learning

Abstract

Reinforcement learning is applied as a model free control method to solve the problem of co channel interference in cellular networks. However, in value based reinforcement learning algorithms, error in function approximation leads to overestimation of the Q value, which leads to the algorithm converging to a suboptimal strategy and poor performance in suppressing channel interference, and the convergence speed is slow in high-frequency scenarios. This paper proposes a control method suitable for distributed deployment, which uses DDQN to learn discrete strategies, and adds a delay-depth deterministic strategy gradient algorithm with a triplet criticism mechan-司轲，李烨