Avancerad sökning

Visar resultat 1 - 5 av 45 uppsatser som matchar ovanstående sökkriterier.

1. 1. Deep reinforcement learning for automated building climate control

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Erik Snällfot; Martin Hörnberg; [2024]
   Nyckelord :Machine Learning; Reinforcement Learning; Deep Learning; Deep Reinforcement Learning; Building Control; Control System;

   Sammanfattning : The building sector is the single largest contributor to greenhouse gas emissions, making it a natural focal point for reducing energy consumption. More efficient use of energy is also becoming increasingly important for property managers as global energy prices are skyrocketing. LÄS MER

3. 2. 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

4. 3. 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

5. 4. Scalable Reinforcement Learning for Linear-Quadratic Control of Networks

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

   Författare :Johan Olsson; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. LÄS MER

7. 5. Scalable Reinforcement Learning for Formation Control with Collision Avoidance : Localized policy gradient algorithm with continuous state and action space

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

   Författare :Andreu Matoses Gimenez; [2023]
   Nyckelord :Control theory; Multi-agent systems; Distributed systems; Formation control; Collision avoidance; Reinforcement learning; Teoria de control; Sistemes multiagent; Sistemes distribuïts; Control de formació; Prevenció de col·lisions; Reinforcement Learning; Reglerteknik; Multi-agent system; Distribuerade system; formationskontroll; Kollisionsundvikande; Reinforcement learning; Teoría de control; Sistemas multiagente; Sistemas distribuidos; Control de formación; Prevención de colisiones; Reinforcement Learning;

   Sammanfattning : In the last decades, significant theoretical advances have been made on the field of distributed mulit-agent control theory. One of the most common systems that can be modelled as multi-agent systems are the so called formation control problems, in which a network of mobile agents is controlled to move towards a desired final formation. LÄS MER