Sökning: "Deep Deterministic Policy Gradient"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden Deep Deterministic Policy Gradient.

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. 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

4. 3. Reinforcement Learning for Hydrobatic AUVs

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

   Författare :Grzegorz Woźniak; [2022]
   Nyckelord :Deep Reinforcement learning; Deep learning; Optimal control; Hydrobatics; Deep Reinforcement learning; Deep learning; Optimal control; Hydrobatics;

   Sammanfattning : This master thesis focuses on developing a Reinforcement Learning (RL) controller to perform hydrobatic maneuvers on an Autonomous Underwater Vehicle (AUV) successfully. This work also aims to analyze the robustness of the RL controller, as well as provide a comparison between RL algorithms and Proportional Integral Derivative (PID) control. LÄS MER

5. 4. 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

7. 5. Link Adaptation in 5G Networks : Reinforcement Learning Framework based Approach

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

   Författare :Siva Satya Sri Ganesh Seeram; [2022]
   Nyckelord :Link Adaptation; OLLA; AMC; Reinforcement Learning; DDPG; BLER; Länkanpassning; OLLA; AMC; förstärkningsinlärning; DDPG; BLER;

   Sammanfattning : Link Adaptation is a core feature introduced in gNodeB (gNB) for Adaptive Modulation and Coding (AMC) scheme in new generation cellular networks. The main purpose of this is to correct the estimated Signal-to-Interference-plus-Noise ratio (SINR) at gNB and select the appropriate Modulation and Coding Scheme (MCS) so the User Equipment (UE) can decode the data successfully. LÄS MER