论文标题
多代理导航的环境优化
Environment Optimization for Multi-Agent Navigation
论文作者
论文摘要
尽管空间限制对代理的性能产生了明显的影响，但多代理导航算法设计的传统方法将环境视为固定的限制。然而，手工设计改进的环境布局和结构效率低下且可能昂贵。本文的目的是将环境视为系统级优化问题中的决策变量，在该问题中，代理性能和环境成本都可以考虑到。我们首先提出一个新颖的环境优化问题。我们通过正式证明在哪些条件下显示环境可以改变的同时保证完整性（即所有代理达到其导航目标）。我们的解决方案利用了一种无模型的增强学习方法。为了适应广泛的实施方案，我们包括在线和离线优化，以及离散和连续的环境表示形式。数值结果证实了我们的理论发现并验证了我们的方法。
Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing improved environment layouts and structures is inefficient and potentially expensive. The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost can be accounted for. We begin by proposing a novel environment optimization problem. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach their navigation goals). Our solution leverages a model-free reinforcement learning approach. In order to accommodate a broad range of implementation scenarios, we include both online and offline optimization, and both discrete and continuous environment representations. Numerical results corroborate our theoretical findings and validate our approach.
