{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:12:29Z","timestamp":1775934749113,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031138430","type":"print"},{"value":"9783031138447","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13844-7_47","type":"book-chapter","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T18:18:24Z","timestamp":1659550704000},"page":"493-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-agent Pathfinding with\u00a0Communication Reinforcement Learning and\u00a0Deadlock Detection"],"prefix":"10.1007","author":[{"given":"Zhaohui","family":"Ye","sequence":"first","affiliation":[]},{"given":"Yanjie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ronghao","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jianqi","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"47_CR1","unstructured":"Foerster, J., Assael, I.A., De Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems 29 (2016)"},{"key":"47_CR2","unstructured":"Jiang, J., Dun, C., Huang, T., Lu, Z.: Graph convolutional reinforcement learning. In: International Conference on Learning Representations (2019)"},{"key":"47_CR3","unstructured":"Jiang, J., Lu, Z.: Learning attentional communication for multi-agent cooperation. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"47_CR4","unstructured":"Kim, D., Moon, S., Hostallero, D., Kang, W.J., Lee, T., Son, K., Yi, Y.: Learning to schedule communication in multi-agent reinforcement learning. In: International Conference on Learning Representations (2018)"},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"Li, Q., Gama, F., Ribeiro, A., Prorok, A.: Graph neural networks for decentralized multi-robot path planning. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11785\u201311792. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341668"},{"issue":"3","key":"47_CR6","doi-asserted-by":"publisher","first-page":"5533","DOI":"10.1109\/LRA.2021.3077863","volume":"6","author":"Q Li","year":"2021","unstructured":"Li, Q., Lin, W., Liu, Z., Prorok, A.: Message-aware graph attention networks for large-scale multi-robot path planning. IEEE Robot. Autom. Lett. 6(3), 5533\u20135540 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, B., Zhou, H., Koushik, G., Hebert, M., Zhao, D.: Mapper: multi-agent path planning with evolutionary reinforcement learning in mixed dynamic environments. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11748\u201311754. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340876"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Long, P., Fan, T., Liao, X., Liu, W., Zhang, H., Pan, J.: Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6252\u20136259. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8461113"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Ma, Z., Luo, Y., Ma, H.: Distributed heuristic multi-agent path finding with communication. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8699\u20138705. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560748"},{"issue":"3","key":"47_CR10","doi-asserted-by":"publisher","first-page":"4249","DOI":"10.1109\/LRA.2020.2994035","volume":"5","author":"B Riviere","year":"2020","unstructured":"Riviere, B., H\u00f6nig, W., Yue, Y., Chung, S.J.: Glas: global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning. IEEE Robot. Automation Lett. 5(3), 4249\u20134256 (2020)","journal-title":"IEEE Robot. Automation Lett."},{"issue":"3","key":"47_CR11","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/LRA.2019.2903261","volume":"4","author":"G Sartoretti","year":"2019","unstructured":"Sartoretti, G., Kerr, J., Shi, Y., Wagner, G., Kumar, T.S., Koenig, S., Choset, H.: Primal: Pathfinding via reinforcement and imitation multi-agent learning. IEEE Robot. Automation Lett. 4(3), 2378\u20132385 (2019)","journal-title":"IEEE Robot. Automation Lett."},{"key":"47_CR12","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.artint.2014.11.006","volume":"219","author":"G Sharon","year":"2015","unstructured":"Sharon, G., Stern, R., Felner, A., Sturtevant, N.R.: Conflict-based search for optimal multi-agent pathfinding. Artif. Intell. 219, 40\u201366 (2015)","journal-title":"Artif. Intell."},{"key":"47_CR13","unstructured":"Sheng, J., et al.: Learning structured communication for multi-agent reinforcement learning. arXiv preprint arXiv:2002.04235 (2020)"},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Silver, D.: Cooperative pathfinding. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 1, pp. 117\u2013122 (2005)","DOI":"10.1609\/aiide.v1i1.18726"},{"key":"47_CR15","unstructured":"Sukhbaatar, S., Fergus, R., et al.: Learning multiagent communication with backpropagation. In: Advances in Neural Information Processing Systems 29 (2016)"},{"issue":"1","key":"47_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learning Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Networks Learning Syst."},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, Y., Liu, Q., Gao, J., Liu, Y., Chen, M.: Multi-agent pathfinding with local and global guidance. In: 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 1\u20137 (2021)","DOI":"10.1109\/ICNSC52481.2021.9702234"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13844-7_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T15:04:20Z","timestamp":1676300660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13844-7_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138430","9783031138447"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13844-7_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"442","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"284","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}