Sökning: "Reinforcement Learning in Real-time"

Visar resultat 1 - 5 av 28 uppsatser innehållade orden Reinforcement Learning in Real-time.

1. 1. Reinforcement learning for EV charging optimization : A holistic perspective for commercial vehicle fleets

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

   Författare :Enzo Alexander Cording; [2023]
   Nyckelord :Deep Reinforcement Learning; EV charging optimization; Artificial Intelligence; Commercial vehicle fleets; Electric vehicles; Deep Reinforcement Learning; optimering av elbilsladdning; artificiell intelligens; kommersiella fordonsflottor; Elektriska fordon;

   Sammanfattning : Recent years have seen an unprecedented uptake in electric vehicles, driven by the global push to reduce carbon emissions. At the same time, intermittent renewables are being deployed increasingly. These developments are putting flexibility measures such as dynamic load management in the spotlight of the energy transition. LÄS MER

3. 2. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

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

   Författare :Shihang Zhou; [2023]
   Nyckelord :Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

   Sammanfattning : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. LÄS MER

4. 3. Cooperative Modular Neural Networks for Artificial Intelligence in Games : A Comparison with A Monolithic Neural Network Regarding Technical Aspects and The Player Experience

   Uppsats för yrkesexamina på avancerad nivå, Blekinge Tekniska Högskola/Fakulteten för datavetenskaper

   Författare :Emil Högstedt; Ove Ødegård; [2023]
   Nyckelord :Neural Network; Modularization; Sensor; Reinforcement Learning; Supervised Learning; Neuralt Nätverk; Modulärisering; Sensor; Förstärkningsinlärning; Väglett Lärande;

   Sammanfattning : Recent years have seen multiple machine-learning research projects concerning agents in video games. Yet, there is a disjoint between this academic research and the video game industry, evidenced by the fact that game developers still hesitate to use neural networks (NN) due to lack of clarity and control. LÄS MER

5. 4. Real-time adaptation of robotic knees using reinforcement control

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

   Författare :Leifur Daníel Sigurðarson; [2023]
   Nyckelord :Machine learning; deep reinforcement learning; transfer learning; medical device; prosthetic; prosthesis; controls; human-in-the-loop; Maskininlärning; djup förstärkningsinlärning; överföringsinlärning; medicinsk utrustning; protes; kontroller; människa-i-loopen;

   Sammanfattning : Microprocessor-controlled knees (MPK’s) allow amputees to walk with increasing ease and safety as technology progresses. As an amputee is fitted with a new MPK, the knee’s internal parameters are tuned to the user’s preferred settings in a controlled environment. LÄS MER

7. 5. An efficient deep reinforcement learning approach to the energy management for a parallel hybrid electric vehicle

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

   Författare :Mingwei Liu; [2023]
   Nyckelord :HEV; EMS; Deep Reinforcement Learning; Learning Efficiency; Fuel Efficiency; HEV; EMS; Djup Förstärkningsinlärning; Inlärningseffektivitet; Bränsleeffektivitet;

   Sammanfattning : In contemporary world, the global energy crisis and raise of greenhouse gas concentration in atmosphere necessitate the energy conservation and emission reduction. Hybrid electric vehicles (HEVs) can achieve great promise in reducing fuel consumption and greenhouse gas emissions by appropriate energy management strategies (EMSs). LÄS MER