  Traditional search and rescue methods in wilderness areas can be
time-consuming and have limited coverage. Drones offer a faster and more
flexible solution, but optimizing their search paths is crucial. This paper
explores the use of deep reinforcement learning to create efficient search
missions for drones in wilderness environments. Our approach leverages a priori
data about the search area and the missing person in the form of a probability
distribution map. This allows the deep reinforcement learning agent to learn
optimal flight paths that maximize the probability of finding the missing
person quickly. Experimental results show that our method achieves a
significant improvement in search times compared to traditional coverage
planning and search planning algorithms. In one comparison, deep reinforcement
learning is found to outperform other algorithms by over $160\%$, a difference
that can mean life or death in real-world search operations. Additionally,
unlike previous work, our approach incorporates a continuous action space
enabled by cubature, allowing for more nuanced flight patterns.
