Essays about: "RL network"
Showing result 16 - 20 of 49 essays containing the words RL network.
-
16. Investigating Multi-Objective Reinforcement Learning for Combinatorial Optimization and Scheduling Problems : Feature Identification for multi-objective Reinforcement Learning models
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Reinforcement Learning (RL) has in recent years become a core method for sequential decision making in complex dynamical systems, being of great interest to support improvements in scheduling problems. This could prove important to areas in the newer generation of cellular networks. READ MORE
-
17. Spiking Reinforcement Learning for Robust Robot Control Under Varying Operating Conditions
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
-
18. Towards Sustainable Mobile Networks: AI for Zero Touch Automated Battery Control
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : With the adaption of 5G technology, a massive increase in the number of devicesand applications that utilize mobile communication systems is expected.The mobile communications sector experiences ever-increasing electricity andcarbon footprint costs due to new services and increased demand. READ MORE
-
19. Benchmarking Deep Reinforcement Learning on Continuous Control Tasks : AComparison of Neural Network Architectures and Environment Designs
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Reinforcement Learning (RL) has received much attention in recent years. This thesis investigates how reward functions, environment termination conditions, Neural Network (NN) architectures, and the type of the deep RL algorithm aect the performance for continuous control tasks. READ MORE
-
20. 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
