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Filtros : "Felizardo, Leonardo Kanashiro" Limpar

Filtros

Tese (Doutorado)
Exploring the boundaries of deep reinforcement learning in simulated environments: a study on financial trading and lot-sizing (2024)
Felizardo, Leonardo Kanashiro ; Del Moral Hernandez, Emilio (Orientador) ; Brandimarte, Paolo (coorient)
Unidade: EP
Assuntos: PESQUISA OPERACIONAL, SISTEMAS AUTÔNOMOS, NEGOCIAÇÃO, SISTEMAS MULTIAGENTES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FELIZARDO, Leonardo Kanashiro. Exploring the boundaries of deep reinforcement learning in simulated environments: a study on financial trading and lot-sizing. 2024. Tese (Doutorado) – Universidade de São Paulo, São Paulo, 2024. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-26082024-093343/pt-br.php. Acesso em: 03 dez. 2025.
APA
Felizardo, L. K. (2024). Exploring the boundaries of deep reinforcement learning in simulated environments: a study on financial trading and lot-sizing (Tese (Doutorado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3142/tde-26082024-093343/pt-br.php
NLM
Felizardo LK. Exploring the boundaries of deep reinforcement learning in simulated environments: a study on financial trading and lot-sizing [Internet]. 2024 ;[citado 2025 dez. 03 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-26082024-093343/pt-br.php
Vancouver
Felizardo LK. Exploring the boundaries of deep reinforcement learning in simulated environments: a study on financial trading and lot-sizing [Internet]. 2024 ;[citado 2025 dez. 03 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-26082024-093343/pt-br.php
Artigo de periodico
Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market (2022)
Felizardo, Leonardo Kanashiro ; Brandimarte, Paolo; Del Moral Hernandez, Emilio ; Reali Costa, Anna Helena ; Matsumoto, Elia Yathie; Paiva, Francisco Caio Lima; Graves, Catharine de Vita
Fonte: Expert Systems with Applications. Unidade: EP
Assunto: APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FELIZARDO, Leonardo Kanashiro et al. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, v. 202, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2022.117259. Acesso em: 03 dez. 2025.
APA
Felizardo, L. K., Brandimarte, P., Del Moral Hernandez, E., Reali Costa, A. H., Matsumoto, E. Y., Paiva, F. C. L., & Graves, C. de V. (2022). Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, 202, 1-13. doi:10.1016/j.eswa.2022.117259
NLM
Felizardo LK, Brandimarte P, Del Moral Hernandez E, Reali Costa AH, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ; 202 1-13.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259
Vancouver
Felizardo LK, Brandimarte P, Del Moral Hernandez E, Reali Costa AH, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ; 202 1-13.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259

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