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Full-Text Articles in Physical Sciences and Mathematics
Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel Alorbani
Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel Alorbani
Electronic Thesis and Dissertation Repository
Today, smart city technology is being adopted by many municipal governments to improve their services and to adapt to growing and changing urban population. Implementing a smart city application can be one of the most challenging projects due to the complexity, requirements and constraints. Sensing devices and computing components can be numerous and heterogeneous. Increasingly, researchers working in the smart city arena are looking to leverage edge and cloud computing to support smart city development. This approach also brings a number of challenges. Two of the main challenges are resource allocation and load balancing of tasks associated with processing data …
Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson
Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson
Electronic Thesis and Dissertation Repository
Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental …
Reinforcement Learning With Motivations For Realistic Agents, Jacquelyne T. Forgette
Reinforcement Learning With Motivations For Realistic Agents, Jacquelyne T. Forgette
Electronic Thesis and Dissertation Repository
Believable virtual humans have important applications in various fields, including computer based video games. The challenge in programming video games is to produce a non-player controlled character that is autonomous, and capable of action selections that appear human. In this thesis, motivations are used as a basis for learning using reinforcements. With motives driving the decisions of the agents, their actions will appear less structured and repetitious, and more human in nature. This will also allow developers to easily create game agents with specific motivations, based mostly on their narrative purposes. With minimum and maximum desirable motive values, the agents …