Multi-agent few-shot meta reinforcement learning for trajectory design and channel selection in UAV-assisted networks

Unmanned aerial vehicle (UAV)-assisted communications have been considered as a solution of aerial networking in future wireless networks due to its low-cost, high-mobility, and swift features. This paper considers a UAV-assisted downlink transmission, where UAVs are deployed as aerial base stations to serve ground users. To maximize the average transmission rate among the ground users, this paper formulates a joint optimization problem of UAV trajectory design and channel selection, which is NP-hard and non-convex. To solve the problem, we propose a multi-agent deep Q-network (MADQN) scheme. Specifically, the agents that the UAVs act as perform actions from their observations distributively and share the same reward. To tackle the tasks where the experience is insufficient, we propose a multi-agent meta reinforcement learning algorithm to fast adapt to the new tasks. By pretraining the tasks with similar distribution, the learning model can acquire general knowledge. Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation. Furthermore, our proposed multiagent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.