  Reinforcement learning algorithms in multi-agent systems deliver highly
resilient and adaptable solutions for common problems in
telecommunications,aerospace, and industrial robotics. However, achieving an
optimal global goal remains a persistent obstacle for collaborative multi-agent
systems, where learning affects the behaviour of more than one agent. A number
of nonlinear function approximation methods have been proposed for solving the
Bellman equation, which describe a recursive format of an optimal policy.
However, how to leverage the value distribution based on reinforcement
learning, and how to improve the efficiency and efficacy of such systems remain
a challenge. In this work, we developed a reward-reinforced generative
adversarial network to represent the distribution of the value function,
replacing the approximation of Bellman updates. We demonstrated our method is
resilient and outperforms other conventional reinforcement learning methods.
This method is also applied to a practical case study: maximising the number of
user connections to autonomous airborne base stations in a mobile communication
network. Our method maximises the data likelihood using a cost function under
which agents have optimal learned behaviours. This reward-reinforced generative
adversarial network can be used as ageneric framework for multi-agent learning
at the system level
