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.

翻译：协作式自主多智能体系统在指定区域覆盖方面具有广泛潜在应用，例如无人机搜救、森林火灾扑救以及实时高分辨率监测。传统方法通常基于传感器数据设计基于模型的控制策略来解决此类覆盖问题。然而，基于模型的控制器设计极具挑战性，且当前最优的经典控制策略仍存在较大程度的次优性。本文针对具有二阶动力学的多智能体高效区域覆盖问题，提出一种基于强化学习的方法。该方法采用多智能体近端策略优化算法（MAPPO），所提出的网络架构整合了LSTM与自注意力机制，使训练后的策略能够适应不同数量的智能体。该训练策略的性能显著优于当前最优的经典控制策略。我们在多种仿真实验中验证了所提方法的有效性。