Essays about: "Markov Decision Process"
Showing result 6 - 10 of 47 essays containing the words Markov Decision Process.
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6. Energy Sustainable Reinforcement Learning-based Adaptive Duty-Cycling in Wireless Sensor Networks-based Internet of Things Networks
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : 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. READ MORE
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7. Improving sample-efficiency of model-free reinforcement learning algorithms on image inputs with representation learning
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : Reinforcement learning struggles to solve control tasks on directly on images. Performance on identical tasks with access to the underlying states is much better. READ MORE
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8. Reasoning about Moving Target Defense in Attack Modeling Formalisms
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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9. Deep Reinforcement Learning for Card Games
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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10. Deep Reinforcement Learning and Simulation for the Optimization of Production Systems
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : 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. READ MORE