Sökning: "Multi-Agent Reinforcement Learning MARL"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Multi-Agent Reinforcement Learning MARL.

1. 1. 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

3. 2. Optimal taxation by two-agent reinforcement learning

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Erik Lindau; [2023]
   Nyckelord :;

   Sammanfattning : An economy’s tax policy is one of the vital moments for, on the one hand, stimulating economic growth and labor, and, on the other hand gaining revenues from economic performance. A sufficient level of tax revenues is further important to keep up with governmental obligations and social welfare. LÄS MER

4. 3. Explainable AI for Multi-Agent Control Problem

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Hanna Prokopova; [2023]
   Nyckelord :;

   Sammanfattning : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. LÄS MER

5. 4. S-MARL: An Algorithm for Single-To-Multi-Agent Reinforcement Learning : Case Study: Formula 1 Race Strategies

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

   Författare :Marinaro Davide; [2023]
   Nyckelord :Reinforcement Learning; Single-to-Multi-Agent; Learning Stability; Exploration-Exploitation trade-off; Race Strategy Optimization; Förstärkningsinlärning; Från en till flera agenter; Stabilitet vid inlärning; Utforskning-exploatering; Optimering av tävlingsstrategier;

   Sammanfattning : A Multi-Agent System is a group of autonomous, intelligent, interacting agents sharing an environment that they observe through sensors, and upon which they act with actuators. The behaviors of these agents can be either defined upfront by programmers or learned by trial-and-error resorting to Reinforcement Learning. LÄS MER

7. 5. 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