A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems (2019)
- Authors:
- USP affiliated authors: COSTA, ANNA HELENA REALI - EP ; SILVA, FELIPE LENO DA - EP
- Unidade: EP
- DOI: 10.1613/jair.1.11396
- Assunto: APRENDIZADO COMPUTACIONAL
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Artificial Intelligence Research
- Volume/Número/Paginação/Ano: v. 64, p. 645-703, 2019
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
SILVA, Felipe Leno da e REALI COSTA, Anna Helena. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. Journal of Artificial Intelligence Research, v. 64, p. 645-703, 2019Tradução . . Disponível em: https://doi.org/10.1613/jair.1.11396. Acesso em: 09 abr. 2026. -
APA
Silva, F. L. da, & Reali Costa, A. H. (2019). A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. Journal of Artificial Intelligence Research, 64, 645-703. doi:10.1613/jair.1.11396 -
NLM
Silva FL da, Reali Costa AH. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems [Internet]. Journal of Artificial Intelligence Research. 2019 ; 64 645-703.[citado 2026 abr. 09 ] Available from: https://doi.org/10.1613/jair.1.11396 -
Vancouver
Silva FL da, Reali Costa AH. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems [Internet]. Journal of Artificial Intelligence Research. 2019 ; 64 645-703.[citado 2026 abr. 09 ] Available from: https://doi.org/10.1613/jair.1.11396 - Building self-play curricula online by playing with expert agents in adversarial games
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- Towards zero-shot autonomous inter-task mapping through object-oriented task description
- Accelerating multiagent reinforcement learning through transfer learning
- Methods and algorithms for knowledge reuse in multiagent reinforcement learning
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- Multi-objective reinforcement learning through reward weighting
- Object-oriented reinforcement learning in cooperative multiagent domains
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