The Drone Swarm Search project is an environment, based on PettingZoo, that
is to be used in conjunction with multi-agent (or single-agent) reinforcement
learning algorithms. It is an environment in which the agents (drones), have to
find the targets (shipwrecked people). The agents do not know the position of
the target and do not receive rewards related to their own distance to the
target(s). However, the agents receive the probabilities of the target(s) being
in a certain cell of the map. The aim of this project is to aid in the study of
reinforcement learning algorithms that require dynamic probabilities as inputs.

该研究旨在研究需要动态概率输入的增强学习算法，实现无人机群体寻找海难人员的模拟环境。

DSSE: 无人机集群搜索环境

DSSE: a drone swarm search environment

Equipping drones with target search capabilities is desirable for
applications in disaster management scenarios and smart warehouse delivery
systems. Instead of deploying a single drone, an intelligent drone swarm that
can collaborate with one another in maneuvering among obstacles will be more
effective in accomplishing the target search in a shorter amount of time. In
this work, we propose a data-efficient reinforcement learning-based approach,
Adaptive Curriculum Embedded Multi-Stage Learning (ACEMSL), to address the
challenges of carrying out a collaborative target search with a visual drone
swarm, namely the 3D sparse reward space exploration and the collaborative
behavior requirement. Specifically, we develop an adaptive embedded curriculum,
where the task difficulty level can be adaptively adjusted according to the
success rate achieved in training. Meanwhile, with multi-stage learning, ACEMSL
allows data-efficient training and individual-team reward allocation for the
collaborative drone swarm. The effectiveness and generalization capability of
our approach are validated using simulations and actual flight tests.

本文提出了一种新的数据高效强化学习方法（Adaptive Curriculum Embedded Multi-Stage Learning，ACEMSL）来解决协作目标搜索的挑战，该方法可在可视无人机群中实现协作任务，并通过模拟和实际飞行测试验证了其有效性和泛化能力。