Multi-objective reinforcement learning through reward weighting (2015)
- Authors:
- USP affiliated authors: COSTA, ANNA HELENA REALI - EP ; SILVA, FELIPE LENO DA - EP
- Unidade: EP
- Subjects: SISTEMAS MULTIAGENTES; APRENDIZADO COMPUTACIONAL
- Keywords: REDES INTELIGENTES
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: International Joint Conferences on Artificial Intelligence
- Publisher place: [S.l.]
- Date published: 2015
- Source:
- Título: TRI at IJCAI 2015
- Conference titles: Workshop on Synergies Between Multiagent Systems, Machine Learning and Complex Systems
-
ABNT
SILVA, Felipe Leno da e REALI COSTA, Anna Helena. Multi-objective reinforcement learning through reward weighting. 2015, Anais.. [S.l.]: International Joint Conferences on Artificial Intelligence, 2015. Disponível em: https://repositorio.usp.br/directbitstream/d96c59d6-71d5-44c7-8091-a4741a54cb88/Multi-Objective-Reinforcement-Learning-through-Reward-Weighting.pdf. Acesso em: 09 abr. 2026. -
APA
Silva, F. L. da, & Reali Costa, A. H. (2015). Multi-objective reinforcement learning through reward weighting. In TRI at IJCAI 2015. [S.l.]: International Joint Conferences on Artificial Intelligence. Recuperado de https://repositorio.usp.br/directbitstream/d96c59d6-71d5-44c7-8091-a4741a54cb88/Multi-Objective-Reinforcement-Learning-through-Reward-Weighting.pdf -
NLM
Silva FL da, Reali Costa AH. Multi-objective reinforcement learning through reward weighting [Internet]. TRI at IJCAI 2015. 2015 ;[citado 2026 abr. 09 ] Available from: https://repositorio.usp.br/directbitstream/d96c59d6-71d5-44c7-8091-a4741a54cb88/Multi-Objective-Reinforcement-Learning-through-Reward-Weighting.pdf -
Vancouver
Silva FL da, Reali Costa AH. Multi-objective reinforcement learning through reward weighting [Internet]. TRI at IJCAI 2015. 2015 ;[citado 2026 abr. 09 ] Available from: https://repositorio.usp.br/directbitstream/d96c59d6-71d5-44c7-8091-a4741a54cb88/Multi-Objective-Reinforcement-Learning-through-Reward-Weighting.pdf - Building self-play curricula online by playing with expert agents in adversarial games
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