ReP USP - Resultado da busca

Dissertação (Mestrado)
Model-based policy gradients: an empirical study on linear quadratic environments (2022)
Lovatto, Ângelo Gregório ; Barros, Leliane Nunes de (Orientador)
Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório. Model-based policy gradients: an empirical study on linear quadratic environments. 2022. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2022. Disponível em: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-28062022-123656/. Acesso em: 28 abr. 2024.
APA
Lovatto, Â. G. (2022). Model-based policy gradients: an empirical study on linear quadratic environments (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/45/45134/tde-28062022-123656/
NLM
Lovatto ÂG. Model-based policy gradients: an empirical study on linear quadratic environments [Internet]. 2022 ;[citado 2024 abr. 28 ] Available from: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-28062022-123656/
Vancouver
Lovatto ÂG. Model-based policy gradients: an empirical study on linear quadratic environments [Internet]. 2022 ;[citado 2024 abr. 28 ] Available from: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-28062022-123656/
Trabalho de evento
Exploration versus exploitation in model-based reinforcement learning: an empirical study (2022)
Lovatto, Ângelo Gregório ; Barros, Leliane Nunes de ; Mauá, Denis Deratani
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Assunto: CONTROLE ÓTIMO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório e BARROS, Leliane Nunes de e MAUÁ, Denis Deratani. Exploration versus exploitation in model-based reinforcement learning: an empirical study. 2022, Anais.. Cham: Springer, 2022. Disponível em: https://doi.org/10.1007/978-3-031-21689-3_3. Acesso em: 28 abr. 2024.
APA
Lovatto, Â. G., Barros, L. N. de, & Mauá, D. D. (2022). Exploration versus exploitation in model-based reinforcement learning: an empirical study. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-21689-3_3
NLM
Lovatto ÂG, Barros LN de, Mauá DD. Exploration versus exploitation in model-based reinforcement learning: an empirical study [Internet]. Proceedings. 2022 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_3
Vancouver
Lovatto ÂG, Barros LN de, Mauá DD. Exploration versus exploitation in model-based reinforcement learning: an empirical study [Internet]. Proceedings. 2022 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_3
Trabalho de evento
Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments (2021)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira ; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório e BUENO, Thiago Pereira e BARROS, Leliane Nunes de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-91702-9_3. Acesso em: 28 abr. 2024.
APA
Lovatto, Â. G., Bueno, T. P., & Barros, L. N. de. (2021). Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-91702-9_3
NLM
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Vancouver
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Trabalho de evento
Decision-aware model learning for actor-critic methods: when theory does not meet practice (2020)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira ; Mauá, Denis Deratani ; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Conference on Neural Information Processing Systems - NeurIPS. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS DE APRENDIZAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório et al. Decision-aware model learning for actor-critic methods: when theory does not meet practice. 2020, Anais.. San Diego: NeurIPS, 2020. Disponível em: https://openreview.net/pdf?id=a9lwn6v40C4. Acesso em: 28 abr. 2024.
APA
Lovatto, Â. G., Bueno, T. P., Mauá, D. D., & Barros, L. N. de. (2020). Decision-aware model learning for actor-critic methods: when theory does not meet practice. In Proceedings. San Diego: NeurIPS. Recuperado de https://openreview.net/pdf?id=a9lwn6v40C4
NLM
Lovatto ÂG, Bueno TP, Mauá DD, Barros LN de. Decision-aware model learning for actor-critic methods: when theory does not meet practice [Internet]. Proceedings. 2020 ;[citado 2024 abr. 28 ] Available from: https://openreview.net/pdf?id=a9lwn6v40C4
Vancouver
Lovatto ÂG, Bueno TP, Mauá DD, Barros LN de. Decision-aware model learning for actor-critic methods: when theory does not meet practice [Internet]. Proceedings. 2020 ;[citado 2024 abr. 28 ] Available from: https://openreview.net/pdf?id=a9lwn6v40C4
Trabalho de evento
Analyzing the effect of stochastic transitions in policy gradients in deep reinforcement learning (2019)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems (BRACIS). Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório e BUENO, Thiago Pereira e BARROS, Leliane Nunes de. Analyzing the effect of stochastic transitions in policy gradients in deep reinforcement learning. 2019, Anais.. Piscataway: IEEE, 2019. Disponível em: https://doi.org/10.1109/BRACIS.2019.00079. Acesso em: 28 abr. 2024.
APA
Lovatto, Â. G., Bueno, T. P., & Barros, L. N. de. (2019). Analyzing the effect of stochastic transitions in policy gradients in deep reinforcement learning. In Proceedings. Piscataway: IEEE. doi:10.1109/BRACIS.2019.00079
NLM
Lovatto ÂG, Bueno TP, Barros LN de. Analyzing the effect of stochastic transitions in policy gradients in deep reinforcement learning [Internet]. Proceedings. 2019 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1109/BRACIS.2019.00079
Vancouver
Lovatto ÂG, Bueno TP, Barros LN de. Analyzing the effect of stochastic transitions in policy gradients in deep reinforcement learning [Internet]. Proceedings. 2019 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1109/BRACIS.2019.00079

USP Schools

ReP

Filtros

Funding Agencies