Sökning: "markov decision process"
Visar resultat 6 - 10 av 50 uppsatser innehållade orden markov decision process.
6. Energy Sustainable Reinforcement Learning-based Adaptive Duty-Cycling in Wireless Sensor Networks-based Internet of Things Networks
Master-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknikSammanfattning : The Internet of Things (IoT) is widely adopted across various fields due to its flexibility and low cost. Energy-harvesting Wireless Sensor Networks (WSNs) are becoming a building block of many IoT applications and provide a perpetual source of energy to power energy-constrained IoT devices. LÄS MER
7. Improving sample-efficiency of model-free reinforcement learning algorithms on image inputs with representation learning
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : Reinforcement learning struggles to solve control tasks on directly on images. Performance on identical tasks with access to the underlying states is much better. LÄS MER
8. Reasoning about Moving Target Defense in Attack Modeling Formalisms
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Since 2009, Moving Target Defense (MTD) has become a new paradigm of defensive mechanism that frequently changes the state of the target system to confuse the attacker. This frequent change is costly and leads to a trade-off between misleading the attacker and disrupting the quality of service. LÄS MER
9. Deep Reinforcement Learning for Card Games
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This project aims to investigate how reinforcement learning (RL) techniques can be applied to the card game LimitTexas Hold’em. RL is a type of machine learning that can learn to optimally solve problems that can be formulated according toa Markov Decision Process. LÄS MER
10. Deep Reinforcement Learning and Simulation for the Optimization of Production Systems
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : The main objective of this master thesis project is to use the deep reinforcement learning (DRL) and simulation method for optimization of production systems. In this project, the Deep Q-learning Networks (DQN) algorithm is first used to optimize seven decision variables in Averill Law’s production system to find the best profit, with 99. LÄS MER