Sökning: "Deterministic Agents"

Visar resultat 1 - 5 av 16 uppsatser innehållade orden Deterministic Agents.

  1. 1. Uncontrolled intersection coordination of the autonomous vehicle based on multi-agent reinforcement learning.

    Master-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Författare :Isaac Arnold McSey; [2023]
    Nyckelord :Autonomous Vehicles AVs ; Road Safety; Fuel Efficiency; Business Dynamics; Intersections; Human-Driven Vehicles HDVs ; Pedestrians; Multi-Agent Reinforcement Learning MARL ; Multi-Agent Deep Deterministic Policy Gradient MADDPG ; Algorithmic Interactions; Uncontrolled Intersections; Global Insights; Safety Improvements; Comfort Improvements; Learning Process; Global Experiences; Complex Environments; Passenger Comfort; Navigation;

    Sammanfattning : This study explores the application of multi-agent reinforcement learning (MARL) to enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs)at uncontrolled intersections. The research aims to assess the potential of MARL in modeling multiple agents interacting within a shared environment, reflecting real-world situations where AVs interact with multiple actors. LÄS MER

  3. 2. Investigation on stability of Knowledge Based Subset Construction in Multi-Agent Games

    Kandidat-uppsats, KTH/Datavetenskap

    Författare :Gustaf Johansson; Gustaf Bergmark; [2022]
    Nyckelord :Multi-Agent games; Imperfect information; Strategy synthesis; Structural conditions; Fleragentsspel; Ofullständig information; Strategisyntes; Strukturella villkor;

    Sammanfattning : Many real life problems can be modelled using multi-agent games played on finite graphs. When an agent cannot differentiate between game states, for example when a robot operates with a broken sensor, the game is classified as a game of imperfect information. LÄS MER

  4. 3. Deep Reinforcement Learning Approach to Portfolio Optimization

    Kandidat-uppsats, Lunds universitet/Nationalekonomiska institutionen

    Författare :Lorik Sadriu; [2022]
    Nyckelord :Deep Reinforcement Learning; Portfolio Optimization; Portfolio performance; EMH; Business and Economics;

    Sammanfattning : This paper evaluates whether a deep reinforcement learning (DRL) approach can be implemented, on the Swedish stock market, to optimize a portfolio. The objective is to create and train two DRL algorithms that can construct portfolios that will be benchmarked against the market portfolio, tracking OMXS30, and the two conventional methods, the naive portfolio, and minimum variance portfolio. LÄS MER

  5. 4. Interpretations of epistemic mu-calculus over multi-agent games

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Nikitas Stathatos; [2022]
    Nyckelord :epistemic logic; game theory; multi-agent systems; semantics; epistemisk logik; spelteori; multi-agent systems; semantiker;

    Sammanfattning : In this work, we are interested in expressing and studying certain formal properties of multi-agent games. In particular, we are interested in the case in which a team of agents with imperfect information is playing against the environment. LÄS MER

  7. 5. Interaction Aware Decision Making for Automated Vehicles Based on Reinforcement Learning

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

    Författare :Ning Wang; [2022]
    Nyckelord :Reinforcement Learning; Automated Vehicles; Decision-making; Rollout; Driver Behaviour Modeling; Trajectory Prediction; Förstärkningsinlärning; automatiserade fordon; beslutsfattande; utrullning; modellering av förarbeteende; banförutsägelse;

    Sammanfattning : Decision-making is one of the key challenges blocking full autonomy of automated vehicles. In highway scenarios, automated vehicles are expected to be aware of their surroundings and make decisions by interacting with other road participants to drive safely and efficiently. LÄS MER