Sökning: "RL"

Visar resultat 1 - 5 av 79 uppsatser innehållade ordet RL.

  1. 1. Interactive Ecosystem Simulator

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

    Författare :JOHAN ATTERFORS; CARL HOLMBERG; ALEXANDER HUANG; ROBIN KARHU; BRAGE LAE; ALEXANDER LARSSON VAHLBERG; [2021-09-07]
    Nyckelord :Ecosystem; Simulation; Unity; AI; ML; RL; ABM; GA; PCG; Welford;

    Sammanfattning : As ecosystems are complex domains, both analytical and computer-aided modelscan aid in gaining insights about their dynamics. One such computer-aided modelis the concept of ecosystem simulation. This project aims to build an interactiveand visual ecosystem simulation in the Unity game engine. LÄS MER

  2. 2. Exploring feasibility of reinforcement learning flight route planning

    Kandidat-uppsats, Linköpings universitet/Institutionen för datavetenskap; Linköpings universitet/Filosofiska fakulteten

    Författare :Axel Wickman; [2021]
    Nyckelord :SAAB; flight route planning; autorouting; auto-routing; auto routing; AI; machine learning; fighter jet; convolution; PPO; DQN; Astar; A*; C ; Python; LibTorch; PyTorch; multi threading; multi-threading; simulation; aerodynamics; world generation; Perlin noise; investigation; reward; Flygplanering; flygruttsplannering; maskininlärning; AI; SAAB; faltning; faltningslager; belöning;

    Sammanfattning : This thesis explores and compares traditional and reinforcement learning (RL) methods of performing 2D flight path planning in 3D space. A wide overview of natural, classic, and learning approaches to planning s done in conjunction with a review of some general recurring problems and tradeoffs that appear within planning. LÄS MER

  3. 3. Application of Deep Q-learning for Vision Control on Atari Environments

    Master-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik

    Författare :Jim Öhman; [2021]
    Nyckelord :Reinforcement learning; Atari 2600; Deep Q-learning; Myopic Agents; Vision Control; Physics and Astronomy;

    Sammanfattning : The success of Reinforcement Learning (RL) has mostly been in artificial domains, with only some successful real-world applications. One of the reasons being that most real-world domains fail to satisfy a set of assumptions of RL theory. LÄS MER

  4. 4. Explainable Reinforcement Learning for Risk Mitigation in Human-Robot Collaboration Scenarios

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

    Författare :Alessandro Iucci; [2021]
    Nyckelord :Explainable Reinforcement Learning; Human-Robot Collaboration; Risk Mitigation; Reward Decomposition; Autonomous Policy Explanation; Collaborative Robots; Förklarbar förstärkningslärande; Mänskligt-robot-samarbete; Riskreducering; Reward Decomposition; Autonomous Policy Explanation; Samarbetsrobotar;

    Sammanfattning : Reinforcement Learning (RL) algorithms are highly popular in the robotics field to solve complex problems, learn from dynamic environments and generate optimal outcomes. However, one of the main limitations of RL is the lack of model transparency. This includes the inability to provide explanations of why the output was generated. LÄS MER

  5. 5. Playing Halite IV with Deep Reinforcement Learning

    Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

    Författare :Kim Haapamäki; Jesper Laurell; [2021]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Playing games with reinforcement learning has for years been a target for research and has seen incredible breakthroughs in recent years. Reinforcement learning is a type of machine learning, which can be combined with the concept of deep learning, resulting in what is called deep reinforcement learning. LÄS MER