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The approach melds meta-learning into an RL framework, addressing multiple subproblems inherent to MOO. Furthermore, the precision of solutions is elevated by endowing exact dynamic programming with the prowess of meta-graph neural networks. Empirical results substantiate the supremacy of our methodology over previous RL and heuristics approaches, bridging the chasm between theoretical underpinnings and real-world applicability within this domain.<\/jats:p>","DOI":"10.1007\/s40747-024-01469-1","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T07:01:30Z","timestamp":1716015690000},"page":"5743-5758","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Dynamic programming with meta-reinforcement learning: a novel approach for multi-objective optimization"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3249-8459","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Chengwei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"key":"1469_CR1","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s002910000046","volume":"22","author":"M Ehrgott","year":"2000","unstructured":"Ehrgott M, Gandibleux X (2000) A survey and annotated bibliography of multiobjective combinatorial optimization. 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