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Designing effective routing strategies for mobile wireless networks ischallenging due to the need to seamlessly adapt routing behavior to spatiallydiverse and temporally changing network conditions. In this work, we use deepreinforcement learning (DeepRL) to learn a scalable and generalizablesingle-copy routing strategy for such networks. We make the followingcontributions: i) we design a reward function that enables the DeepRL agent toexplicitly trade-off competing network goals, such as minimizing delay vs. thenumber of transmissions per packet; ii) we propose a novel set of relationalneighborhood, path, and context features to characterize mobile wirelessnetworks and model device mobility independently of a specific networktopology; and iii) we use a flexible training approach that allows us tocombine data from all packets and devices into a single offline centralizedtraining set to train a single DeepRL agent. To evaluate generalizeability andscalability, we train our DeepRL agent on one mobile network scenario and thentest it on other mobile scenarios, varying the number of devices andtransmission ranges. Our results show our learned single-copy routing strategyoutperforms all other strategies in terms of delay except for the optimalstrategy, even on scenarios on which the DeepRL agent was not trained.

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