  Collaborative autonomous multi-agent systems covering a specified area have
many potential applications, such as UAV search and rescue, forest fire
fighting, and real-time high-resolution monitoring. Traditional approaches for
such coverage problems involve designing a model-based control policy based on
sensor data. However, designing model-based controllers is challenging, and the
state-of-the-art classical control policy still exhibits a large degree of
sub-optimality. In this paper, we present a reinforcement learning (RL)
approach for the multi-agent efficient domain coverage problem involving agents
with second-order dynamics. Our approach is based on the Multi-Agent Proximal
Policy Optimization Algorithm (MAPPO). Our proposed network architecture
includes the incorporation of LSTM and self-attention, which allows the trained
policy to adapt to a variable number of agents. Our trained policy
significantly outperforms the state-of-the-art classical control policy. We
demonstrate our proposed method in a variety of simulated experiments.

Robotics;Machine Learning;Multiagent Systems