Coverage path planning (CPP) is the task of designing a trajectory that
enables a mobile agent to travel over every point of an area of interest. We
propose a new method to control an unmanned aerial vehicle (UAV) carrying a
camera on a CPP mission with random start positions and multiple options for
landing positions in an environment containing no-fly zones. While numerous
approaches have been proposed to solve similar CPP problems, we leverage
end-to-end reinforcement learning (RL) to learn a control policy that
generalizes over varying power constraints for the UAV. Despite recent
improvements in battery technology, the maximum flying range of small UAVs is
still a severe constraint, which is exacerbated by variations in the UAV's
power consumption that are hard to predict. By using map-like input channels to
feed spatial information through convolutional network layers to the agent, we
are able to train a double deep Q-network (DDQN) to make control decisions for
the UAV, balancing limited power budget and coverage goal. The proposed method
can be applied to a wide variety of environments and harmonizes complex goal
structures with system constraints.

本文提出了一种新方法，利用端到端强化学习通过地图通道提供空间信息并使用 DDQN 平衡有限电力预算和无人机的航程，以控制在一些禁飞区域有随机起飞位置和多个降落选项的环境中的无人机 CNN 航迹规划任务，此方法可以应用于各种环境且很好地协调了复杂的系统约束与目标结构。

利用深度强化学习进行变化功率限制下的无人机覆盖路径规划

UAV Coverage Path Planning under Varying Power Constraints using Deep  Reinforcement Learning

While selecting the hyper-parameters of Neural Networks (NNs) has been so far
treated as an art, the emergence of more complex, deeper architectures poses
increasingly more challenges to designers and Machine Learning (ML)
practitioners, especially when power and memory constraints need to be
considered. In this work, we propose HyperPower, a framework that enables
efficient Bayesian optimization and random search in the context of power- and
memory-constrained hyper-parameter optimization for NNs running on a given
hardware platform. HyperPower is the first work (i) to show that power
consumption can be used as a low-cost, a priori known constraint, and (ii) to
propose predictive models for the power and memory of NNs executing on GPUs.
Thanks to HyperPower, the number of function evaluations and the best test
error achieved by a constraint-unaware method are reached up to 112.99x and
30.12x faster, respectively, while never considering invalid configurations.
HyperPower significantly speeds up the hyper-parameter optimization, achieving
up to 57.20x more function evaluations compared to constraint-unaware methods
for a given time interval, effectively yielding significant accuracy
improvements by up to 67.6%.

该论文介绍了一种名为 HyperPower 的框架，旨在通过贝叶斯优化和随机搜索实现神经网络超参数优化，并考虑功耗和内存限制。HyperPower 框架显著提高了超参数优化的速度和准确性。