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：协同自主多智能体系统在指定区域覆盖方面具有诸多潜在应用，例如无人机搜救、森林灭火以及实时高分辨率监测。传统方法基于传感器数据设计模型控制策略来解决此类覆盖问题。然而，模型控制器的设计颇具挑战，且当前最先进的经典控制策略仍存在较大程度的次优性。本文提出一种基于强化学习的方法，用于解决涉及二阶动力学智能体的多智能体高效区域覆盖问题。该方法基于多智能体近端策略优化算法。我们提出的网络架构融合了长短期记忆网络与自注意力机制，使训练得到的策略能够适应可变数量的智能体。该训练策略显著优于当前最先进的经典控制策略。我们通过多种仿真实验对所提方法进行了验证。