Building self-play curricula online by playing with expert agents in adversarial games (2019)
- Authors:
- USP affiliated authors: COSTA, ANNA HELENA REALI - EP ; SILVA, FELIPE LENO DA - EP
- Unidade: EP
- DOI: 10.1109/BRACIS.2019.00090
- Subjects: APRENDIZADO COMPUTACIONAL; ALGORITMOS; AGENTES INTELIGENTES; JOGOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2019
- Source:
- Título: Proceedings
- Conference titles: Brazilian Conference on Intelligent Systems (BRACIS)
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
SILVA, Felipe Leno da e REALI COSTA, Anna Helena e STONE, Peter. Building self-play curricula online by playing with expert agents in adversarial games. 2019, Anais.. Piscataway: Escola Politécnica, Universidade de São Paulo, 2019. Disponível em: https://doi.org/10.1109/BRACIS.2019.00090. Acesso em: 09 abr. 2026. -
APA
Silva, F. L. da, Reali Costa, A. H., & Stone, P. (2019). Building self-play curricula online by playing with expert agents in adversarial games. In Proceedings. Piscataway: Escola Politécnica, Universidade de São Paulo. doi:10.1109/BRACIS.2019.00090 -
NLM
Silva FL da, Reali Costa AH, Stone P. Building self-play curricula online by playing with expert agents in adversarial games [Internet]. Proceedings. 2019 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1109/BRACIS.2019.00090 -
Vancouver
Silva FL da, Reali Costa AH, Stone P. Building self-play curricula online by playing with expert agents in adversarial games [Internet]. Proceedings. 2019 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1109/BRACIS.2019.00090 - Autonomously reusing knowledge in multiagent reinforcement learning
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- Methods and algorithms for knowledge reuse in multiagent reinforcement learning
- Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues
- A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
- Distributional reinforcement learning applied to robot soccer simulation
- Multi-objective reinforcement learning through reward weighting
- Object-oriented reinforcement learning in cooperative multiagent domains
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