  In this paper, we propose a distributed solution to design a multi-hop ad hoc
network where mobile relay nodes strategically determine their wireless
transmission ranges based on a deep reinforcement learning approach. We
consider scenarios where only a limited networking infrastructure is available
but a large number of wireless mobile relay nodes are deployed in building a
multi-hop ad hoc network to deliver source data to the destination. A mobile
relay node is considered as a decision-making agent that strategically
determines its transmission range in a way that maximizes network throughput
while minimizing the corresponding transmission power consumption. Each relay
node collects information from its partial observations and learns its
environment through a sequence of experiences. Hence, the proposed solution
requires only a minimal amount of information from the system. We show that the
actions that the relay nodes take from its policy are determined as to activate
or inactivate its transmission, i.e., only necessary relay nodes are activated
with the maximum transmit power, and nonessential nodes are deactivated to
minimize power consumption. Using extensive experiments, we confirm that the
proposed solution builds a network with higher network performance than current
state-of-the-art solutions in terms of system goodput and connectivity ratio.
