{"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/cooperative-multi-agent-reinforcement-5","title":"Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment","arxiv_id":null,"date":"2021-05-21","proceeding":"NeurIPS 2021 12","authors":["Yifan Zang","Jinmin He","Kai Li","Lily Cao","Haobo Fu","Qiang Fu","Junliang Xing"],"abstract":"Centralized training with decentralized execution is a standard paradigm for cooperative multi-agent reinforcement learning (MARL), with credit assignment being a major challenge. In this paper, we propose a cooperative MARL method with sequential credit assignment (SeCA) that deduces each agent's contribution to the team's success one by one to learn better cooperation. We first present a sequential MARL framework, under which we introduce a new counterfactual advantage to evaluate each agent based on its preceding agents' actions in a specific sequence. As this credit assignment sequence tremendously impacts the performance, we further present a sequence adjustment algorithm utilizing integrated gradients. It dynamically modifies the sequence among agents according to their contribution to the team. SeCA employs a network which either estimates the Q value for training the centralized critic or deduces the proposed advantage of each agent for decentralized policy learning. Our method is evaluated on a challenging set of StarCraft II micromanagement tasks and achieves state-of-the-art performance. ","url_abs":"https://openreview.net/forum?id=IQgbmaoDDjd","url_pdf":"https://openreview.net/pdf?id=IQgbmaoDDjd","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":"cooperative-multi-agent-reinforcement-5","repo_url":"https://github.com/mzho7212/LICA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"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)"},{"task_slug":"starcraft","task_name":"Starcraft"},{"task_slug":"starcraft-ii","task_name":"Starcraft II"},{"task_slug":null,"task_name":"counterfactual"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}