{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T06:56:19Z","timestamp":1730271379720,"version":"3.28.0"},"reference-count":10,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,6]]},"DOI":"10.1109\/isamsr53229.2021.9567835","type":"proceedings-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T17:14:52Z","timestamp":1635268492000},"page":"84-90","source":"Crossref","is-referenced-by-count":4,"title":["A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions"],"prefix":"10.1109","author":[{"given":"Fumito","family":"Uwano","sequence":"first","affiliation":[]}],"member":"263","reference":[{"journal-title":"Rainbow Combining Improvements in Deep Reinforcement Learning","year":"2018","author":"hessel","key":"ref4"},{"key":"ref3","first-page":"447","author":"ghosh","year":"2020","journal-title":"Towards Deployment of Robust Cooperative AI Agents An Algorithmic Framework for Learning Adaptive Policies"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.9746\/jcmsi.11.321"},{"journal-title":"Proximal policy optimization algorithms","year":"2017","author":"schulman","key":"ref6"},{"key":"ref5","volume":"abs 1602 1783","author":"mnih","year":"2016","journal-title":"Asynchronous methods for deep reinforcement learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref7","article-title":"Chainerrl: A deep reinforcement learning library","author":"fujita","year":"0","journal-title":"Neural Information Processing Systems Conference Deep Reinforcement Learning Workshop"},{"key":"ref2","first-page":"4257","article-title":"Modeling others using oneself in multi-agent reinforcement learning","volume":"80","author":"raileanu","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref1","first-page":"1242","article-title":"Adaptive traffic signal control using distributed marl and federated learning","author":"wang","year":"0","journal-title":"2020 IEEE 20th International Conference on Communication Technology (ICCT)"}],"event":{"name":"2021 4th International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR)","start":{"date-parts":[[2021,9,6]]},"location":"Batu Pahat, Malaysia","end":{"date-parts":[[2021,9,8]]}},"container-title":["2021 4th International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9567714\/9567738\/09567835.pdf?arnumber=9567835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:53:23Z","timestamp":1652187203000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9567835\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":10,"URL":"https:\/\/doi.org\/10.1109\/isamsr53229.2021.9567835","relation":{},"subject":[],"published":{"date-parts":[[2021,9,6]]}}}