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Reconfigurable intelligent surface and UAV-assisted communications: a deep reinforcement learning approach Khoi Khac Nguyen (24165060) PUREID: 304345540 This thesis proposes novel methods based on the deep reinforcement learning algorithms (DRL) for maximising the energy efficiency (EE), sum-rate in reconfigurable intelligent surface (RIS) and unmanned aerieal vehicles (UAV)-aided wireless communications. The thesis carries out comprehensive optimization and evaluation of various DRL algorithms for several real-life applications including UAV's trajectory design, power allocation, data collection, wireless power transfer and RIS's phase shift matrix adjustment.<br><br>The thesis presents three major contributions. Firstly, we design a new UAV-assisted Internet-of -things (IoT) system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. then, a DRL-based technique is conceived for finding 2026-10-01T17:10:39Z Text Thesis 10.17034/32632542.v1 https://figshare.com/articles/thesis/Reconfigurable_intelligent_surface_and_UAV-assisted_communications_a_deep_reinforcement_learning_approach/32632542 All Rights Reserved