  Interference among concurrent transmissions in a wireless network is a key
factor limiting the system performance. One way to alleviate this problem is to
manage the radio resources in order to maximize either the average or the
worst-case performance. However, joint consideration of both metrics is often
neglected as they are competing in nature. In this article, a mechanism for
radio resource management using multi-agent deep reinforcement learning (RL) is
proposed, which strikes the right trade-off between maximizing the average and
the $5^{th}$ percentile user throughput. Each transmitter in the network is
equipped with a deep RL agent, receiving partial observations from the network
(e.g., channel quality, interference level, etc.) and deciding whether to be
active or inactive at each scheduling interval for given radio resources, a
process referred to as link scheduling. Based on the actions of all agents, the
network emits a reward to the agents, indicating how good their joint decisions
were. The proposed framework enables the agents to make decisions in a
distributed manner, and the reward is designed in such a way that the agents
strive to guarantee a minimum performance, leading to a fair resource
allocation among all users across the network. Simulation results demonstrate
the superiority of our approach compared to decentralized baselines in terms of
average and $5^{th}$ percentile user throughput, while achieving performance
close to that of a centralized exhaustive search approach. Moreover, the
proposed framework is robust to mismatches between training and testing
scenarios. In particular, it is shown that an agent trained on a network with
low transmitter density maintains its performance and outperforms the baselines
when deployed in a network with a higher transmitter density.
