Use Of Reinforcement Learning for Prioritizing Communications in Contested and Dynamic Environments
Dustin Craggs, Kin Leong Lee, Vanja Radenovic, Benjamin Campbell, Claudia Szabo · 2022
Systems operating in military operations and crisis situations usually do so in contested and dynamic environments with poor and unreliable network conditions. Individual nodes within these systems usually have an incomplete, local and changing view of the system and its operating environment, and as such optimizing how nodes communicate in order to improve decision making is critical. In this paper, we propose the integration of reinforcement learning algorithms with the SMARTNet middleware, a middleware that prioritizes and controls messages sent by each node, allowing it to determine the best priority for each message type. We experiment with both direct and indirect prioritisation approaches, where the reinforcement learning dissemination system determines the specific priority of a message on its arrival or upon its sending respectively. Our experimental analysis show significant improvements over the baseline in some of the high congestion scenarios but also highlights several avenues for future work.