{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-cooperative-multi-agent-reinforcement","title":"A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network","arxiv_id":"1903.00714","date":"2019-03-02","proceeding":null,"authors":["Xihan Li","Jia Zhang","Jiang Bian","Yunhai Tong","Tie-Yan Liu"],"abstract":"Resource balancing within complex transportation networks is one of the most\nimportant problems in real logistics domain. Traditional solutions on these\nproblems leverage combinatorial optimization with demand and supply\nforecasting. However, the high complexity of transportation routes, severe\nuncertainty of future demand and supply, together with non-convex business\nconstraints make it extremely challenging in the traditional resource\nmanagement field. In this paper, we propose a novel sophisticated multi-agent\nreinforcement learning approach to address these challenges. In particular,\ninspired by the externalities especially the interactions among resource\nagents, we introduce an innovative cooperative mechanism for state and reward\ndesign resulting in more effective and efficient transportation. Extensive\nexperiments on a simulated ocean transportation service demonstrate that our\nnew approach can stimulate cooperation among agents and lead to much better\nperformance. Compared with traditional solutions based on combinatorial\noptimization, our approach can give rise to a significant improvement in terms\nof both performance and stability.","url_abs":"http://arxiv.org/abs/1903.00714v1","url_pdf":"http://arxiv.org/pdf/1903.00714v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-cooperative-multi-agent-reinforcement","repo_url":"https://github.com/microsoft/maro","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"},{"task_slug":"management","task_name":"Management"},{"task_slug":"multi-agent-reinforcement-learning","task_name":"Multi-agent Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.00714","atlas_url":"https://app.syntology.ai/?focus=1903.00714","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}