Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

Mobile wireless networks present several challenges for any learning system, due to uncertain and variable device movement, a decentralized network architecture, and constraints on network resources. In this work, we use deep reinforcement learning (DRL) to learn a scalable and generalizable forwarding strategy for such networks. We make the following contributions: i) we use hierarchical RL to design DRL packet agents rather than device agents, to capture the packet forwarding decisions that are made over time and improve training efficiency; ii) we use relational features to ensure generalizeability of the learned forwarding strategy to a wide range of network dynamics and enable offline training; and iii) we design the DRL reward function to reflect both the packet forwarding goals and the resource considerations of the network; and we incorporate both the forwarding goals and network resource considerations into packet decision-making by designing a weighted DRL reward function. Our results show that our DRL agent often achieves a similar delay per packet delivered as the optimal forwarding strategy and outperforms all other strategies including state-of-the art strategies, even on scenarios on which the DRL agent was not trained.