Sökning: "DDPG"

Visar resultat 1 - 5 av 13 uppsatser innehållade ordet DDPG.

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. 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

4. 3. 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

5. 4. Spiking Reinforcement Learning for Robust Robot Control Under Varying Operating Conditions

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

   Författare :Philipp Mondorf; [2022]
   Nyckelord :;

   Sammanfattning : Over the last few years, deep reinforcement learning (RL) has gained increasing popularity for its successful application to a variety of complex control and decision-making tasks. As the demand for deep RL algorithms deployed in challenging real-world environments grows, their robustness towards uncertainty, disturbances and perturbations of the environment becomes more and more important. LÄS MER

7. 5. Deep Reinforcement Learning for Dynamic Grasping

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Andreas Ström; [2022]
   Nyckelord :Deep Reinforcement Learning; Dynamic Grasping; DDPG; HER; Robotics;

   Sammanfattning : Dynamic grasping is the action of, using only contact force, manipulating the position of a moving object in space. Doing so with a robot is a quite complex task in itself, but is one with wide-ranging applications. LÄS MER