Found 239 repositories(showing 30)
aws-samples
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aws-samples
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卡车-无人机协同配送系统，通过多智能体强化学习(MAPPO)算法优化物流配送效率。系统模拟真实的城市配送场景，其中卡车作为移动补给站，无人机执行最后一公里配送任务。optimized using Multi-Agent Reinforcement Learning (MAPPO) algorithms to enhance logistics delivery efficiency. The system simulates real-world urban delivery scenarios where trucks serve as mobile supply stations, and drones carry out the last-mile delivery tasks
aws-samples
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Multi-Agent Deep Reinforcement Learning for Collaborative Computation Offloading in Mobile Edge-Computing
awsfundamentals-hq
🤖 A Simple Multi-Agent Bedrock Application with SST
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Project explores collaboration capabilities of VDN and IQL agents on a custom MARL Food Collector environment
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Hands-On Guide: How Multi-AI Agents Collaborate to Build Winning Product Development Strategies
一个基于agentscope架构的多智能体狼人杀游戏。在该项目中，你既可以作为旁观者，观看agent扮演不同角色进行狼人杀游戏，也可以随机扮演其中一名角色参与到游戏中。
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ealfcast
Multi-agent Collaboration reference Code and patterns for FSI
Multi-Agent RL for Negotiation and Collaboration
Guanys-dar
File-based protocol for AI coding agents (Claude Code, Codex, Gemini CLI) to collaborate on a shared codebase with cross-review
Janaelpardisi
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This Repository Contains PyTorch Implementation of Multi Agent DDPG Algorithm to train agents to play tennis.
这是一个面向医疗场景的多智能体 AI 系统，集成文档检索、医学影像分析、实时研究跟踪和语音交互，确保生成结果安全可靠并可经专家验证。
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在教育领域快速发展的当下，课程内容的及时更新是一项至关重要的任务，尤其在计算机科学和人工智能等知识更新迅速的学科中。然而，传统的课程内容修订方式不仅工作量大、耗时长，而且难以与最新的学术成果和行业实践保持同步。此外，直接利用智能体工具生成的课程内容往往结构混乱，质量难以保证。针对这些问题，本文提出了一种多智能体协同的课程内容动态更新方法。该方法通过融合多智能体和知识图谱技术，实现了对最新知识点的自动化检索、筛选和整合，进而生成结构化、准确且全面的课程内容体系。这一方法不仅有效减轻了教育工作者的负担，还显著提高了课程内容更新的效率和质量，为教育内容管理提供了一种全新的解决方案。研究结果表明，与传统人工修订和直接调用智能体的方式相比，该方法在知识点的准确性、覆盖率和执行周期方面均取得了显著的改进
taitiant
一个简单的多智能体协作系统
EducloudHQ
A Multi-Agent Collaboration Application with AWS Bedrock, Pinecone, python and CDK
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san99tiago
DEMO for a simple multi-agent collaboration solution on AWS with new Bedrock capabilities
The Zhuge Alliance Multi-Agent Collaboration Tool is an innovative software that integrates multiple advanced AI model services and has powerful multi-agent collaboration capabilities. It supports the simultaneous invocation of well-known AI models such as DeepSeek, Qwen, and Moonshot Kimi.
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Multi-Agent DDPG algorithm for solving a collaboration and competition problem in a Unity simulation
Two competitive reinforcement learning agents learn by themselves to play tennis
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