Sökning: "reinforcement learning"

Visar resultat 11 - 15 av 239 uppsatser innehållade orden reinforcement learning.

  1. 11. Safe Reinforcement Learning for Remote Electrical Tilt Optimization

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

    Författare :Grigorios Iakovidis; [2021]
    Nyckelord :Remote Electrical Tilt; Antenna Tilt Optimization; Reinforcement Learning; SafeReinforcement Learning; Fjärrlutning; Antennlutningsoptimering; Förstärkningsinlärning; Säker Förstärkningsinlärning;

    Sammanfattning : The adjustment of the vertical tilt angle of Base Station (BS) antennas, also known as Remote Electrical Tilt (RET) optimization, is a simple and efficient method of optimizing modern telecommunications networks. Reinforcement Learning (RL) is a machine learning framework that can solve complex problems like RET optimization due to its capability to learn from experience and adapt to dynamic environments. LÄS MER

  2. 12. Maskininlärningsmetoder tillämpade på StarCraft 2 - En undersökning av reinforcement och imitation learning

    Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :JONATHAN BERGQVIST; CARL CLAESSON; PONTUS ELIASSON; ADAM GRANDÉN; EDVIN LAM; ARVID LUNDBERG; [2020-10-29]
    Nyckelord :;

    Sammanfattning : Inom artificiell intelligens, som kontinuerligt utvecklas, har maskininlärning tagit en centralroll. Medan regelbaserad AI varit tillräcklig för att lösa grundläggande uppgifter behöverdagens utmaningar mer avancerade metoder. LÄS MER

  3. 13. Resource Optimal Neural Networks for Safety-critical Real-time Systems

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Joakim Åkerström; [2020-07-10]
    Nyckelord :Data science; machine learning; deep learning; neural networks; network compression; network acceleration; safety-critical systems; real-time systems;

    Sammanfattning : Deep neural networks consume an excessive amount of hardware resources, makingthem difficult to deploy to real-time systems. Previous work in the field of networkcompression lack the explicit hardware feedback necessary to control the resourceconstraints imposed by such systems. LÄS MER

  4. 14. Deep Reinforcement Learning in Cart Pole and Pong

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

    Författare :Dennis Kuurne Uussilta; Viktor Olsson; [2020]
    Nyckelord :Artificial Intelligence; Machine Learning; Rein-forcement Learning; Deep Q-learning Network; CartPole; Pong;

    Sammanfattning : In this project, we aim to reproduce previous resultsachieved with Deep Reinforcement Learning. We present theMarkov Decision Process model as well as the algorithms Q-learning and Deep Q-learning Network (DQN). We implement aDQN agent, first in an environment called CartPole, and later inthe game Pong. LÄS MER

  5. 15. Deep Reinforcement Learning for Complete Coverage Path Planning in Unknown Environments

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

    Författare :Omar Boufous; [2020]
    Nyckelord :;

    Sammanfattning : Mobile robots must operate autonomously, often in unknown and unstructured environments. To achieve this objective, a robot must be able to correctly per- ceive its environment, plan its path, and move around safely, without human su- pervision. Navigation from an initial position to a target location has been a chal- lenging problem in robotics. LÄS MER