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 pa-per considers a UAV-assisted downlink transmission,where UAVs are deployed as aerial base stations to serve ground users.To maximize the average trans-mission rate among the ground users,this paper for-mulates a joint optimization problem of UAV trajec-tory 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 per-form 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 sim-ilar distribution,the learning model can acquire gen-eral knowledge.Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation.Furthermore,our proposed multi-agent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.