  We investigate the problem of wireless routing in integrated access backhaul
(IAB) networks consisting of fiber-connected and wireless base stations and
multiple users. The physical constraints of these networks prevent the use of a
central controller, and base stations have limited access to real-time network
conditions. We aim to maximize packet arrival ratio while minimizing their
latency, for this purpose, we formulate the problem as a multi-agent partially
observed Markov decision process (POMDP). To solve this problem, we develop a
Relational Advantage Actor Critic (Relational A2C) algorithm that uses
Multi-Agent Reinforcement Learning (MARL) and information about similar
destinations to derive a joint routing policy on a distributed basis. We
present three training paradigms for this algorithm and demonstrate its ability
to achieve near-centralized performance. Our results show that Relational A2C
outperforms other reinforcement learning algorithms, leading to increased
network efficiency and reduced selfish agent behavior. To the best of our
knowledge, this work is the first to optimize routing strategy for IAB
networks.
