传感器到像素：基于图像强化学习的去中心化蜂群聚集
Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning
AI总结：
本研究提出了一种基于图像强化学习的去中心化蜂群聚集方法，通过结构化视觉输入提升多智能体系统的收敛效率。
摘要：
本研究强调了基于图像的强化学习方法在解决蜂群相关任务中的潜力。在多智能体强化学习中，有效的策略学习依赖于智能体如何感知、解释和处理输入。传统方法往往依赖于手工特征提取或原始向量表示，这限制了学习策略在输入顺序和大小方面的可扩展性和效率。在本工作中，我们提出了一种基于图像的强化学习方法，用于多智能体系统的去中心化控制，其中观察被编码为结构化的视觉输入，可以由神经网络处理，提取其空间特征，并生成新的去中心化运动控制规则。我们在具有有限范围和方位仅感知的多智能体收敛任务中评估了我们的方法，这些任务旨在在聚集过程中保持蜂群的凝聚力。该算法的性能在两个基准上进行评估：Bellaiche和Bruckstein提出的解析解，该解确保收敛但进展缓慢，以及VariAntNet，一个基于神经网络的框架，收敛速度更快但在复杂排列中表现中等。我们的方法实现了高收敛性，速度接近VariAntNet。在某些情况下，它是唯一实用的替代方案。
英文摘要：
This study highlights the potential of image-based reinforcement learning methods for addressing swarm-related tasks. In multi-agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process inputs. Traditional approaches often rely on handcrafted feature extraction or raw vector-based representations, which limit the scalability and efficiency of learned policies concerning input order and size. In this work we propose an image-based reinforcement learning method for decentralized control of a multi-agent system, where observations are encoded as structured visual inputs that can be processed by Neural Networks, extracting its spatial features and producing novel decentralized motion control rules. We evaluate our approach on a multi-agent convergence task of agents with limited-range and bearing-only sensing that aim to keep the swarm cohesive during the aggregation. The algorithm's performance is evaluated against two benchmarks: an analytical solution proposed by Bellaiche and Bruckstein, which ensures convergence but progresses slowly, and VariAntNet, a neural network-based framework that converges much faster but shows medium success rates in hard constellations. Our method achieves high convergence, with a pace nearly matching that of VariAntNet. In some scenarios, it serves as the only practical alternative.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
