Multiagent-Cooperation:多主体合作论文项目

多agent 中文版 pdf原来英文版翻译multi－agent Wooldridge

低阶多智能体系统matlab仿真，主要是一阶多智能体的包含控制，以及没有包含的例子

multiagent-particle-envs-master.zip

(Princeton Series in Applied Mathematics) Mehran Mesbahi, Magnus Egerstedt-Graph Theoretic Methods in Multiagent Networks (Princeton Series in Applied Mathematics)-Princeton University Press (2010)

CBBA-Python 这是基于共识的捆绑算法（CBBA）的Python实现。 您可以从这些论文中查看有关CBBA的更多详细信息。 要求：Python> = 3.7 此仓库已通过以下测试： Python 3.9.1，macOS 11.2.1，numpy 1.20.1，matplotlib 3.3.4 python 3.8.5，Ubuntu 20.04.2 LTS，numpy 1.20.1，matplotlib 3.3.4 依存关系 对于Python： $ pip3 install numpy matplotlib 用法 任务和代理的参数写在配置json文件中。 AGENT_TYPES ：座席类型。 TASK_TYPES ：任务类型。 第i个任务类型与第i个代理类型相关联。 NOM_VELOCITY ：代理的平均速度[m / s]。 FUEL ：代理商的旅行罚金/费

Wiley - An Introduction To Multiagent Systems [Wooldridge] 英文pdf 清晰版

城流 CityFlow 是一种用于大规模城市交通场景的多智能体强化学习环境。 检查这些功能！ 一种微观交通模拟器，可模拟每辆车的行为，提供最高级别的交通演变细节。 支持灵活定义路网和交通流 为强化学习提供友好的python接口 快速地！ 精心设计的数据结构和多线程仿真算法。 能够模拟城市范围内的交通。 请参阅与 SUMO 的性能比较。 具有不同线程数（1、2、4、8）和 SUMO 的 CityFlow 之间的性能比较。 从小型 1x1 网格路网到城市级 30x30 路网。 当您需要通过 python API 与模拟器交互时，速度会更快。 截屏 使用 CityFlow 的特色研究和项目 链接 WWW 2019 演示文稿 主页 文档和快速入门 码头工人 [1] 相扑首页 [2] 天让智能首页

用于多无人机对抗的多主体强化学习算法 这是“在战斗任务中进行多智能体强化学习的有效培训技术”的源代码，我们构建了源自多个无人驾驶飞机的战斗场景的多智能体对抗环境。 首先，我们考虑使用两种类型的MARL算法来解决这一对抗问题。 一种是从用于多代理设置（MADQN）的经典深度Q网络扩展而来的。 另一个是从最新的多主体强化方法，多主体深度确定性策略梯度（MADDPG）扩展而来。 我们比较了两种方法的初始对抗情况，发现MADDPG的性能优于MADQN。 然后以MADDPG为基准，提出了三种有效的训练技术，即场景转移训练，自学训练和规则耦合训练。 规则耦合红色特工vs随机移动蓝色特工 规则耦合的红色特工和蓝色特工通过自我比赛训练

This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: University of Stuttgart, Germany Royal Institute of Technology, Sweden Georgia Tech, USA University of Washington, USA Ohio University, USA