{"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/multi-agent-reinforcement-learning-a-report","title":"Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches","arxiv_id":"1807.09427","date":"2018-07-25","proceeding":null,"authors":["Sanyam Kapoor"],"abstract":"Reinforcement Learning (RL) is a learning paradigm concerned with learning to\ncontrol a system so as to maximize an objective over the long term. This\napproach to learning has received immense interest in recent times and success\nmanifests itself in the form of human-level performance on games like\n\\textit{Go}. While RL is emerging as a practical component in real-life\nsystems, most successes have been in Single Agent domains. This report will\ninstead specifically focus on challenges that are unique to Multi-Agent Systems\ninteracting in mixed cooperative and competitive environments. The report\nconcludes with advances in the paradigm of training Multi-Agent Systems called\n\\textit{Decentralized Actor, Centralized Critic}, based on an extension of MDPs\ncalled \\textit{Decentralized Partially Observable MDP}s, which has seen a\nrenewed interest lately.","url_abs":"http://arxiv.org/abs/1807.09427v1","url_pdf":"http://arxiv.org/pdf/1807.09427v1.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":"multi-agent-reinforcement-learning-a-report","repo_url":"https://github.com/activatedgeek/marl-challenges-approaches","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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":"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}