{"id":46336229,"url":"https://github.com/yunxiaoguo/abcs-flocking","last_synced_at":"2026-03-04T19:01:20.911Z","repository":{"id":246692983,"uuid":"776830556","full_name":"YunxiaoGuo/ABCS-Flocking","owner":"YunxiaoGuo","description":"The Code of the paper: \"An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning\", which implemented fixed-wing UAV/Drone flocking with collision-free.","archived":false,"fork":false,"pushed_at":"2025-11-21T00:18:45.000Z","size":1553,"stargazers_count":25,"open_issues_count":2,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-21T02:25:41.447Z","etag":null,"topics":["drones","flocking","multi-agent-reinforcement-learning","swarm","uavs"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/YunxiaoGuo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-03-24T15:12:13.000Z","updated_at":"2025-11-21T00:36:48.000Z","dependencies_parsed_at":"2024-06-29T18:38:45.566Z","dependency_job_id":"5ea91098-1e57-4a97-b78e-d9ae9c035612","html_url":"https://github.com/YunxiaoGuo/ABCS-Flocking","commit_stats":null,"previous_names":["yunxiaoguo/abcs-flocking"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/YunxiaoGuo/ABCS-Flocking","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YunxiaoGuo%2FABCS-Flocking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YunxiaoGuo%2FABCS-Flocking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YunxiaoGuo%2FABCS-Flocking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YunxiaoGuo%2FABCS-Flocking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/YunxiaoGuo","download_url":"https://codeload.github.com/YunxiaoGuo/ABCS-Flocking/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YunxiaoGuo%2FABCS-Flocking/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30090037,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T18:31:08.343Z","status":"ssl_error","status_checked_at":"2026-03-04T18:31:07.708Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["drones","flocking","multi-agent-reinforcement-learning","swarm","uavs"],"created_at":"2026-03-04T19:01:18.373Z","updated_at":"2026-03-04T19:01:20.167Z","avatar_url":"https://github.com/YunxiaoGuo.png","language":"Python","readme":"# An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning\n\n\n\u003e This is the **Attention Based Cucker-Smale Flocking algorithm** (A Multi-Agent Reinforcement Learning Algorithm for UAV/Drone Flocking) implementation on [Multi-Agent Particle Environment(MPE)](https://github.com/openai/multiagent-particle-envs), the corresponding paper is [An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning](https://doi.org/10.1016/j.engappai.2025.111797) \n\nThe MADDPG part is come from: [MADDPG](https://gitee.com/ming_autumn/MADDPG-1?_from=gitee_search)\n\n\n## Requirements\n\n### Key Requirements\n\n- python\u003e=3.6.5 (we recommend python==3.9)\n- [Multi-Agent Particle Environment(MPE)](https://github.com/openai/multiagent-particle-envs)\n- torch=1.1.0\n\nThe full requirements can be installed by:\n\n```\npip install -r requirements.txt\n```\n\n**If you are troubled in installation of the requirements. We provide the Anaconda-python environment that you can download directly: [LG-CS.zip](https://pan.baidu.com/s/1ODtPNWxLOWAHcw7ZDz2sWw/MARL)**\n\n**Extract code: MARL**\n\n## Complie Cython Code\nBefore running the code, please complie the environment code (Or download the complied version (`.pyd` file) from: [release](https://github.com/YunxiaoGuo/ABCS-Flocking/releases), and put `.pyd` in `./envs`):\n\n```shell\ncd ./envs\npython setup.py build_ext --inplace --force\n```\n\n## Training Agents\nRunning the main.py, the agents will learn from the flocking scenario：\n```shell\npython main.py --n-agents=5 --evaluate-episodes=256\n```\nIf you want to adjust the parameters, please see the `./common/arguments.py` for more details.\n\n\n\n## Testing Agents\n```shell\npython main.py --n-agents=5 --evaluate-episodes=10 --evaluate=True\n```\n\n## Display Results\n\nAfter data collection:\n\n```shell\npython display.py\n```\n\n# Citation\n\nIf you use this code, please cite our paper:\n```\n[1] Yunxiao Guo, Dan Xu, Chang Wang, Jinxi Li, Han Long, An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning,Engineering Applications of Artificial Intelligence,\n2025,160, Part C,111797,doi: 10.1016/j.engappai.2025.111797. \n```\nBibtex form:\n```\n@article{GUO2025111797,\nauthor = {Yunxiao Guo and Dan Xu and Chang Wang and Jinxi Li and Han Long},\ntitle = {An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning},\njournal = {Engineering Applications of Artificial Intelligence},\nvolume = {160},\npages = {111797},\nyear = {2025},\nissn = {0952-1976},\ndoi = {https://doi.org/10.1016/j.engappai.2025.111797},\nurl = {https://www.sciencedirect.com/science/article/pii/S0952197625017993}\n}\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyunxiaoguo%2Fabcs-flocking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyunxiaoguo%2Fabcs-flocking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyunxiaoguo%2Fabcs-flocking/lists"}