A framework to discover and reuse object-oriented options in reinforcement learning (2018)
- Authors:
- USP affiliated authors: SPINA, EDISON - EP ; COSTA, ANNA HELENA REALI - EP ; BONINI, RODRIGO CESAR - EP ; SILVA, FELIPE LENO DA - EP ; GLATT, RUBEN - EP
- Unidade: EP
- DOI: 10.1109/BRACIS.2018.00027
- Subjects: APRENDIZADO COMPUTACIONAL; AGENTES INTELIGENTES; PROGRAMAÇÃO ORIENTADA A OBJETOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2018
- Source:
- Título: Proceedings
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
BONINI, Rodrigo Cesar et al. A framework to discover and reuse object-oriented options in reinforcement learning. 2018, Anais.. Piscataway: Escola Politécnica, Universidade de São Paulo, 2018. Disponível em: https://doi.org/10.1109/BRACIS.2018.00027. Acesso em: 13 fev. 2026. -
APA
Bonini, R. C., Silva, F. L. da, Glatt, R., Spina, E., & Reali Costa, A. H. (2018). A framework to discover and reuse object-oriented options in reinforcement learning. In Proceedings. Piscataway: Escola Politécnica, Universidade de São Paulo. doi:10.1109/BRACIS.2018.00027 -
NLM
Bonini RC, Silva FL da, Glatt R, Spina E, Reali Costa AH. A framework to discover and reuse object-oriented options in reinforcement learning [Internet]. Proceedings. 2018 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/BRACIS.2018.00027 -
Vancouver
Bonini RC, Silva FL da, Glatt R, Spina E, Reali Costa AH. A framework to discover and reuse object-oriented options in reinforcement learning [Internet]. Proceedings. 2018 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/BRACIS.2018.00027 - Object-oriented reinforcement learning in cooperative multiagent domains
- Towards knowledge transfer in deep reinforcement learning
- An advising framework for multiagent reinforcement learning systems
- MOO-MDP: an Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning
- Building self-play curricula online by playing with expert agents in adversarial games
- Autonomously reusing knowledge in multiagent reinforcement learning
- Accelerating multiagent reinforcement learning through transfer learning
- 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
Informações sobre o DOI: 10.1109/BRACIS.2018.00027 (Fonte: oaDOI API)
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