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Visar resultat 1 - 5 av 58 uppsatser som matchar ovanstående sökkriterier.

1. 1. Evaluation of Deep Q-Learning Applied to City Environment Autonomous Driving

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och system

   Författare :Jonas Wedén; [2024]
   Nyckelord :Machine Learning; ML; Reinforcement Learning; RL; Neural Network; Deep Learning; Autonomous Vehicle; Vehicle; CARLA; Convolutional Neural Network; CNN; Precisit; Q-learning; Deep Q-learning; DQN;

   Sammanfattning : This project’s goal was to assess both the challenges of implementing the Deep Q-Learning algorithm to create an autonomous car in the CARLA simulator, and the driving performance of the resulting model. An agent was trained to follow waypoints based on two main approaches. LÄS MER

3. 2. LEO Satellite Connectivity for flying vehicles

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

   Författare :Jinxuan Chen; [2023]
   Nyckelord :LEO satellite network; satellite connectivity strategy; Nash-SAC; flying vehicles; LEO:s satellitnät; Strategi för satellitanslutning; Nash-SAC; flygande fordon;

   Sammanfattning : Compared with the terrestrial network (TN), which can only support limited covered areas, satellite communication (SC) can provide global coverage and high survivability in case of an emergency like an earthquake. Especially low-earth orbit (LEO) satellites, as a promising technology, which is integral to achieving the goal of global seamless coverage and reliable communication, catering to 6G’s communication requirements. LÄS MER

4. 3. Multi-Agent Deep Reinforcement Learning in Warehouse Environments

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

   Författare :John Cao; Mikael Hammarling; [2023]
   Nyckelord :;

   Sammanfattning : This report presents a deep reinforcement algorithm for multi-agent systems based on the classicalDeep Q-Learning algorithm. The method considers a decentralized approach to controlling theagents, by equipping each agent with its own neural network and replay memory. LÄS MER

5. 4. Deep Reinforcement Learning in Games Based on Extracted Features

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

   Författare :Emilia Sjögren; Erika Weidenhaijn; [2023]
   Nyckelord :;

   Sammanfattning : FlappyBird is a popular mobile game that captured many people's attention because itwas easy to understand but difficult to perform --- players were often right on the edge ofsucceeding, which led to a strong desire to play again. The purpose of this project is to investigatethe possibility of using a neural network trained with reinforcement learning to play the game usingextracted features rather than raw images. LÄS MER

7. 5. Federated Machine Learning for Resource Allocation in Multi-domain Fog Ecosystems

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Weilin Zhang; [2023]
   Nyckelord :Workload Allocation; Federated Learning; Deep Q-network; Fog networks; Federated Average Aggregation;

   Sammanfattning : The proliferation of the Internet of Things (IoT) has increasingly demanded intimacy between cloud services and end-users. This has incentivised extending cloud resources to the edge in what is deemed fog computing. The latter is manifesting as an ecosystem of connected clouds, geo-dispersed and of diverse capacities. LÄS MER