The Application of Multi Agent Reinforcement Learning in UAV Networks
This article investigates autonomous resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long term rewards. To model the uncertainty of environments, we formulate the long term resource allocation problem as a stochastic game, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Furthermore, we propose a multi agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q learning. Finally, simulation results reveal that the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs.
2019 IEEE International Conference on Communications Workshops (ICC Workshops)