Reward tuning for self-adaptive policy in MDP based distributed decision-making to ensure a safe mission planning (2020)
- Authors:
- Autor USP: BRANCO, KALINKA REGINA LUCAS JAQUIE CASTELO - ICMC
- Unidade: ICMC
- DOI: 10.1109/DSN-W50199.2020.00025
- Subjects: PROCESSOS DE MARKOV; SISTEMAS DISTRIBUÍDOS; AERONAVES NÃO TRIPULADAS
- Keywords: Markov Decision Process; Concurrent Actions; Reward Tuning; Behavior Conflicts; Constraints on MDPs
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2020
- Source:
- Título: Proceedings
- Conference titles: Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops - DSN-W
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
HAMADOUCHE, Mohand e DEZAN, Catherine e BRANCO, Kalinka Regina Lucas Jaquie Castelo. Reward tuning for self-adaptive policy in MDP based distributed decision-making to ensure a safe mission planning. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/DSN-W50199.2020.00025. Acesso em: 11 nov. 2024. -
APA
Hamadouche, M., Dezan, C., & Branco, K. R. L. J. C. (2020). Reward tuning for self-adaptive policy in MDP based distributed decision-making to ensure a safe mission planning. In Proceedings. Los Alamitos: IEEE. doi:10.1109/DSN-W50199.2020.00025 -
NLM
Hamadouche M, Dezan C, Branco KRLJC. Reward tuning for self-adaptive policy in MDP based distributed decision-making to ensure a safe mission planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 11 ] Available from: https://doi.org/10.1109/DSN-W50199.2020.00025 -
Vancouver
Hamadouche M, Dezan C, Branco KRLJC. Reward tuning for self-adaptive policy in MDP based distributed decision-making to ensure a safe mission planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 11 ] Available from: https://doi.org/10.1109/DSN-W50199.2020.00025 - Índices de carga e desempenho em ambientes paralelos/distribuídos: modelagem e métricas
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Informações sobre o DOI: 10.1109/DSN-W50199.2020.00025 (Fonte: oaDOI API)
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