  Equipping drones with target search capabilities is highly desirable for
applications in disaster rescue and smart warehouse delivery systems. Multiple
intelligent drones that can collaborate with each other and maneuver among
obstacles show more effectiveness in accomplishing tasks in a shorter amount of
time. However, carrying out collaborative target search (CTS) without prior
target information is extremely challenging, especially with a visual drone
swarm. In this work, we propose a novel data-efficient deep reinforcement
learning (DRL) approach called adaptive curriculum embedded multistage learning
(ACEMSL) to address these challenges, mainly 3-D sparse reward space
exploration with limited visual perception and collaborative behavior
requirements. Specifically, we decompose the CTS task into several subtasks
including individual obstacle avoidance, target search, and inter-agent
collaboration, and progressively train the agents with multistage learning.
Meanwhile, an adaptive embedded curriculum (AEC) is designed, where the task
difficulty level (TDL) can be adaptively adjusted based on the success rate
(SR) achieved in training. ACEMSL allows data-efficient training and
individual-team reward allocation for the visual drone swarm. Furthermore, we
deploy the trained model over a real visual drone swarm and perform CTS
operations without fine-tuning. Extensive simulations and real-world flight
tests validate the effectiveness and generalizability of ACEMSL. The project is
available at https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git.
