Essays about: "Deep Q-learning Network"
Showing result 11 - 15 of 33 essays containing the words Deep Q-learning Network.
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11. Application of Deep Q-learning for Vision Control on Atari Environments
University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationAbstract : The success of Reinforcement Learning (RL) has mostly been in artificial domains, with only some successful real-world applications. One of the reasons being that most real-world domains fail to satisfy a set of assumptions of RL theory. READ MORE
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12. Control of an Inverted Pendulum Using Reinforcement Learning Methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this paper the two reinforcement learning algorithmsQ-learning and deep Q-learning (DQN) are used tobalance an inverted pendulum. In order to compare the two, bothalgorithms are optimized to some extent, by evaluating differentvalues for some parameters of the algorithms. READ MORE
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13. A Comparison Between Deep Q-learning and Deep Deterministic Policy Gradient for an Autonomous Drone in a Simulated Environment
University essay from Mälardalens högskola/Akademin för innovation, design och teknikAbstract : This thesis investigates how the performance between Deep Q-Network (DQN) with a continuous and discrete state- and action space, respectively, and Deep Deterministic Policy Gradient (DDPG) with a continuous state- and action space compare when trained in an environment with a continuous state- and action space. The environment was a simulation where the task for the algorithms was to control a drone from the start position to the location of the goal. READ MORE
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14. AI-driven admission control : with Deep Reinforcement Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 5G is expected to provide a high-performance and highly efficient network to prominent industry verticals with ubiquitous access to a wide range of services with orders of magnitude of improvement over 4G. Network slicing, which allocates network resources according to users’ specific requirements, is a key feature to fulfil the diversity of requirements in 5G network. READ MORE
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15. Offline Reinforcement Learning for Remote Electrical Tilt Optimization : An application of Conservative Q-Learning
University essay from KTH/Matematik (Avd.)Abstract : In telecom networks adjusting the tilt of antennas in an optimal manner, the so called remote electrical tilt (RET) optimization, is a method to ensure quality of service (QoS) for network users. Tilt adjustments made during operations in real-world networks are usually executed through a suboptimal policy, and a significant amount of data is collected during the execution of such policy. READ MORE
