This paper addresses data transmission challenges in UAV-assisted wireless networks where multiple unmanned aerial vehicles serve as relays between ground users and a remote base station. The proposed delay-tolerant multi-agent deep reinforcement learning algorithm jointly optimizes trajectory planning, network formation, and transmission control while incorporating a delay-penalized reward mechanism to encourage inter-UAV information sharing. To handle information loss from unreliable channel conditions, a spatio-temporal attention mechanism predicts and recovers missing network state information, enhancing each UAV's situational awareness for improved collaboration and overall network throughput.