  In this paper, we propose a novel joint intelligent trajectory design and
resource allocation algorithm based on user's mobility and their requested
services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act
as nodes of a network function virtualization (NFV) enabled network. Our
objective is to maximize energy efficiency and minimize the average delay on
all services by allocating the limited radio and NFV resources. In addition,
due to the traffic conditions and mobility of users, we let some Virtual
Network Functions (VNFs) to migrate from their current locations to other
locations to satisfy the Quality of Service requirements. We formulate our
problem to find near-optimal locations of UAVs, transmit power, subcarrier
assignment, placement, and scheduling the requested service's functions over
the UAVs and perform suitable VNF migration. Then we propose a novel
Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement
learning method to solve our problem. Finally, the convergence and
computational complexity of the proposed algorithm and its performance analyzed
for different parameters. Simulation results show that our proposed HHCDA
method decreases the request reject rate and average delay by 31.5% and 20% and
increases the energy efficiency by 40% compared to DDPG method.
