{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:19:51Z","timestamp":1756991991021},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>Multi-agent reinforcement learning commonly uses a global team reward signal to represent overall collaborative performance. Value decomposition breaks this global reward into estimated individual value functions per agent, enabling efficient training. However, in sparse reward environments, agents struggle to assess if their actions achieve the team goal, slowing convergence. This impedes the algorithm\u2019s convergence rate and overall efficacy. We present IPERS, an Individual Prioritized Experience Replay algorithm with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning. IPERS integrates joint action decomposition and prioritized experience replay, maintaining invariance between global and individual loss gradients. Subgoals serve as intermediate goals that break down complex tasks into simpler steps with dense feedback and provide helpful intrinsic rewards that guide agents. This facilitates learning coordinated policies in challenging collaborative environments with sparse rewards. Experimental evaluations of IPERS in both the SMAC and GRF environments demonstrate rapid adaptation to diverse multi-agent tasks and significant improvements in win rate and convergence performance relative to state-of-the-art algorithms.<\/jats:p>","DOI":"10.3233\/faia230586","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:20:40Z","timestamp":1695979240000},"source":"Crossref","is-referenced-by-count":1,"title":["IPERS: Individual Prioritized Experience Replay with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning"],"prefix":"10.3233","author":[{"given":"Zaipeng","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China"},{"name":"College of Computer and Information, Hohai University, Nanjing, China"}]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing, China"}]},{"given":"Chentai","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing, China"}]},{"given":"Sitong","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230586","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:20:41Z","timestamp":1695979241000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230586"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230586","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}