Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle by Reza Jafari (3494018)

“…To this end, three model-free DRL-based methods, based on deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and TD3 enhanced with curriculum learning (CL TD3), are developed for determining optimal yaw moment control and energy optimization online. …”

Adaptive temperature control of a reverse flow process by using reinforcement learning approach by A. Binid (22046054)

“…First, a policy iteration algorithm is introduced to learn the optimal solution of the associated linear-quadratic control problem online. …”

DRL-Based IRS-Assisted Secure Visible Light Communications by Danya A. Saifaldeen (19498705)

“…The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users’ mobility. …”

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications by Danya A. Saifaldeen (19498705)

“…To address this complexity optimally, we propose a deep reinforcement learning (DRL) approach based on the deep deterministic policy gradient (DDPG) technique. …”

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”

Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems by Jaber Al-Khori (16855470)

Iterative Least Squares Functional Networks Classifier by Faisal, Kanaan A

“…Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. …”
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Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

“…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. …”
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Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

“…Given the complexity of the problem and the incomplete knowledge about the environment, the problem is divided into two subproblems: the first models the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot as a Markov Decision Process, and uses Proximal Policy Optimization to solve it. The second device is then selected using a heuristic algorithm based on prioritizing devices with higher bit rate requirements and strict deadlines. …”
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Studying cooperative multi-agent reinforcement learning in networks by Oudah, Mayada Mohamed

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Reinforcement Learning-Based School Energy Management System by Yassine Chemingui (18891757)

“…After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. …”

A learning approach for prioritized handoff channel allocation in mobile multimedia networks by El-Alfy, E.-S.M.

“…Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies.…”
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Impacts of On-Grid Solar PV on Distribution Networks and Potential Solutions: A Case Study in the Region of Zahle by Korkmaz, Jessica

“…The siting and sizing methodology is conducted by considering five different optimization algorithms, namely the single-objective genetic algorithm (SOGA), the combined SOGA and loss sensitivity factor algorithm (SOGA-LSF), the multi-objective genetic algorithm (MOGA), the combined MOGA-LSF and the CAPADD algorithm of OpenDSS. …”
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AI-Augmented Metasurface Synthesis for Dynamic Beam Steering in Reconfigurable Antenna Arrays by Bostani, Ali

“…As compared to the conventional heuristic methods, for example, genetic algorithms (GA), particle swarm optimization (PSO), the approach based on DRLs has rapid policy convergence, has relatively less computational latency, and is autonomous to adapt to dynamic wireless conditions. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management by Alhusin, Mohammed Omer Alamin

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A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks by Sakib Mahmud (15302404)

“…We present a structured taxonomy covering value-based, policy-based, actor-critic, model-based, and advanced multi-agent and multi-objective approaches, and link algorithms to tasks such as dispatch, microgrid coordination, real-time pricing, load balancing, and demand–response. …”

Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters by Farhan, Abid

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