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Visar resultat 1 - 5 av 10 uppsatser som matchar ovanstående sökkriterier.

1. 1. Bayesian Networks for Modelling the Respiratory System and Predicting Hospitalizations

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Victor Lopo Martinez; [2023]
   Nyckelord :Bayesian Networks; Structure Learning; Conditional Probability Tables; Maximum Likelihood Estimator; XGBoost; and Respiratory System; Bayesianska nätverk; Strukturinlärning; Villkorliga sannolikhetstabeller; Maximum Likelihood Estimator; XGBoost; och Andningssystemet;

   Sammanfattning : Bayesian networks can be used to model the respiratory system. Their structure indicate how risk factors, symptoms, and diseases are related and the Conditional Probability Tables enable predictions about a patient’s need for hospitalization. LÄS MER

3. 2. Model-based Residual Policy Learning for Sample Efficient Mobile Network Optimization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Viktor Eriksson Möllerstedt; [2022]
   Nyckelord :Reinforcement Learning; Sample Efficiency; Model-based; Expert Policy; Remote Electrical Tilt; Telecommunication; Förstärkande inlärning; dataeffektivitet; modell-baserad; expert-policy; fjärrstyrning av antenners nedåtlutning; telekommunikation;

   Sammanfattning : Reinforcement learning is a powerful tool which enables an agent to learn how to control complex systems. However, during the early phases of training, the performance is often poor. LÄS MER

4. 3. Scaling up Maximum Entropy Deep Inverse Reinforcement Learning with Transfer Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Emil Broqvist Widham; [2020]
   Nyckelord :;

   Sammanfattning : In this thesis an issue with common inverse reinforcement learning algorithms is identified, which causes them to be computationally heavy. A solution is proposed which attempts to address this issue and which can be built upon in the future. LÄS MER

5. 4. Power Plant Operation Optimization Economic dispatch of combined cycle power plants

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Stefano Rosso; [2019]
   Nyckelord :;

   Sammanfattning : As electricity production from renewable sources increases, higher flexibility is required by fossil fuel generation to cope with the inherent fluctuations of solar and wind power. This results in shorter operating cycles and steeper ramps for the turbines, and more uncertainty for the operators. LÄS MER

7. 5. Tactical route planning in battlefield simulations with inverse reinforcement learning

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Emil Broqvist Widham; [2019]
   Nyckelord :;

   Sammanfattning : In this report Deep Maximum Entropy Inverse Reinforcement Learning has been applied to the problem of route planning in rough terrain, while taking tactical parameters into account. The tactical parameters that the report focuses on is to avoid detection from predetermined static observers by keeping blocking terrain in the sight line. LÄS MER