{"type":"ScholarlyArticle","title":"N-Agent Ad Hoc Teamwork","authors":[{"name":"Caroline Wang","slug":"caroline-wang","given":"Caroline","family":"Wang","affiliation":"Department of Computer Science The University of Texas at Austin"}],"abstract":"Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a single agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario,  a company might train its cars to cooperate with each other, yet once on the road, these cars must additionally cooperate with cars from other companies. Towards expanding the class of scenarios that cooperative learning methods may optimally address, this research agenda introduces and proposes to study N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates.","doi":"10.24963/ijcai.2024/974","url":"https://ijcai.org/proceedings/2024/974","pdf":"https://ijcai.org/proceedings/2024/0974.pdf","markdown":"https://ijcai.org/proceedings/2024/974.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/974","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":974,"pages":{"first":8518,"last":8519},"keywords":["DC: Machine Learning","DC: Agent-based and Multi-agent Systems"],"videos":[],"references":[{"position":1,"raw":"[Littman, 1994] Michael L Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, pages 157-163. Elsevier, 1994.","title":"Markov games as a framework for multi-agent reinforcement learning","authors":"Michael L Littman","year":"1994","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Papoudakis et al., 2021] Georgios Papoudakis, Filippos Christianos, and Stefano V. Albrecht. Agent modelling under partial observability for deep reinforcement learn- ing. In Advances in Neural Information Processing Systems, 2021.","title":"Agent modelling under partial observability for deep reinforcement learning","authors":"Georgios Papoudakis, Filippos Christianos, Stefano V Albrecht","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Rashid et al., 2018] Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning, volume 80, 2018.","title":"QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning","authors":"Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, Shimon Whiteson","year":"2018","venue":"Proceedings of the 35th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Stone et al., 2010] Peter Stone, Gal Kaminka, Sarit Kraus, and Jeffrey Rosenschein. Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, 2010.","title":"Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination","authors":"Peter Stone, Gal Kaminka, Sarit Kraus, Jeffrey Rosenschein","year":"2010","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Yu et al., 2022] Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu. The surprising effectiveness of mappo in cooperative multi-agent games. In Proceedings of the Neural Information Processing Sys- tems Track on Datasets and Benchmarks, 2022.","title":"The surprising effectiveness of mappo in cooperative multi-agent games","authors":"Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu","year":"2022","venue":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}