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Visar resultat 6 - 10 av 50 uppsatser som matchar ovanstående sökkriterier.

  1. 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 rymdteknik

    Författare :Nadia Charef; [2023]
    Nyckelord :Reinforcement Learning; Q-learning; Dynamic Energy Management; Energy Sustainabiltiy; IEEE802.15.4 MAC Protocol; Adaptive Duty Cycling; Wireless Sensors Networks; Internet of Things;

    Sammanfattning : 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

  3. 7. Improving sample-efficiency of model-free reinforcement learning algorithms on image inputs with representation learning

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Marko Guberina; Betelhem Dejene Desta; [2022-10-14]
    Nyckelord :sample-efficient reinforcement learning; state representation learning; unsupervised learning; autoencoder;

    Sammanfattning : 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

  4. 8. Reasoning about Moving Target Defense in Attack Modeling Formalisms

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

    Författare :Gabriel Ballot; [2022]
    Nyckelord :Timed Model checking; Cyber Security; Threat Modeling; Moving Target Defense; Tidsinställd modellkontroll; Cybersäkerhet; Hotmodellering; Moving Target Defense;

    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

  5. 9. Deep Reinforcement Learning for Card Games

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

    Författare :Oscar Tegnér Mohringe; Rayan Cali; [2022]
    Nyckelord :Reinforcement Learning; Deep Q-Learning; Deep Monte-Carlo; Poker;

    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

  7. 10. Deep Reinforcement Learning and Simulation for the Optimization of Production Systems

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Siyuan Chen; [2022]
    Nyckelord :;

    Sammanfattning : 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