Autonomous mapping of unknown environments is a critical challenge, particularly in scenarios where time is limited. Multi-agent systems can enhance efficiency through collaboration, but the scalability of motion-planning algorithms remains a key limitation. Reinforcement learning has been explored as a solution, but existing approaches are constrained by the limited input size required for effective learning, restricting their applicability to discrete environments. This work addresses that limitation by leveraging autoencoders to perform dimensionality reduction, compressing high-fidelity occupancy maps into latent state vectors while preserving essential spatial information. Additionally, we introduce a novel procedural generation algorithm based on Perlin noise, designed to generate topologically complex training environments that simulate asteroid fields, caves and forests. These environments are used for training the autoencoder and the navigation algorithm using a hierarchical deep reinforcement learning framework for decentralized coordination. We introduce a weighted consensus mechanism that modulates reliance on shared data via a tuneable trust parameter, ensuring robustness to accumulation of errors. Experimental results demonstrate that the proposed system scales effectively with number of agents and generalizes well to unfamiliar, structurally distinct environments and is resilient in communication-constrained settings.

翻译：未知环境的自主测绘是一个关键挑战，在时间受限的场景中尤为如此。多智能体系统可通过协作提升效率，但运动规划算法的可扩展性仍是主要限制因素。强化学习已被探索作为解决方案，但现有方法受限于有效学习所需的有限输入规模，致使其仅适用于离散环境。本研究通过利用自编码器进行降维来解决这一局限，将高保真占据栅格地图压缩为潜在状态向量，同时保留关键空间信息。此外，我们提出一种基于Perlin噪声的新型程序化生成算法，旨在生成拓扑结构复杂的训练环境，模拟小行星带、洞穴和森林等场景。这些环境用于训练自编码器及导航算法，并采用分层深度强化学习框架实现去中心化协同。我们引入一种加权共识机制，通过可调节的信任参数调控对共享数据的依赖程度，确保对误差累积的鲁棒性。实验结果表明，所提出的系统能随智能体数量有效扩展，对结构迥异的陌生环境具有良好的泛化能力，且在通信受限场景中表现出强韧性。