  Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining
growing popularity recently. To solve the underlying task scheduling problem,
the deep reinforcement learning (DRL) based methods demonstrate notable
advantage over the conventional heuristics as they rely less on hand-engineered
rules. However, their decision space will become prohibitively huge as the
problem scales up, thus deteriorating the computation efficiency. To alleviate
this issue, we propose a double-level deep reinforcement learning (DL-DRL)
approach based on a divide and conquer framework (DCF), where we decompose the
task scheduling of multi-UAV into task allocation and route planning.
Particularly, we design an encoder-decoder structured policy network in our
upper-level DRL model to allocate the tasks to different UAVs, and we exploit
another attention based policy network in our lower-level DRL model to
construct the route for each UAV, with the objective to maximize the number of
executed tasks given the maximum flight distance of the UAV. To effectively
train the two models, we design an interactive training strategy (ITS), which
includes pre-training, intensive training and alternate training. Experimental
results show that our DL-DRL performs favorably against the learning-based and
conventional baselines including the OR-Tools, in terms of solution quality and
computation efficiency. We also verify the generalization performance of our
approach by applying it to larger sizes of up to 1000 tasks. Moreover, we also
show via an ablation study that our ITS can help achieve a balance between the
performance and training efficiency.
