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Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization
Published 2023“…The Q value plays a crucial role in estimating future state values within a Kalman filter tracking system. Proximal Policy Optimization (PPO), a state-of-the-art policy optimization algorithm, was employed to determine the optimal Q value that enhances tracking performance, as measured by Root Mean Square Error (RMSE). …”
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Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization
Published 2023“…In particular, the Proximity Policy Optimization (PPO) reinforcement algorithm is used to discover a policy for sensor selection that results in optimum sensor resource allocation. …”
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Integrated economic and environmental models for a multi stage cold supply chain under carbon tax regulation
Published 2017“…The structural properties for the optimal solution of the three models are identified and solution algorithms are also proposed. …”
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Blood Glucose Regulation Modelling and Intelligent Control
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Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks
Published 2021“…Then, we propose a distributed DRL-based algorithm Deep Deterministic Policy Gradient (DDPG), to solve the formulated computationally-intensive problem. …”
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Economic Production Lot-Sizing For An Unreliable Machine Under Imperfect Age-Based Maintenance Policy
Published 2020“…Some useful properties of the cost function are developed to characterize the optimal policy. An algorithm is also proposed to find the optimal solutions to the problem at hand. …”
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Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks
Published 2025“…This paper proposes a novel composite enhanced proximal policy optimization (CePPO) algorithm to improve power grid operation under adversarial conditions. …”
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Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling
Published 2022“…A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. …”
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Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers
Published 2024“…Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. …”
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Multi-Objective Optimization for Food Availability under Economic and Environmental Risk Constraints
Published 2024“…Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. …”
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Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review
Published 2023“…The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. …”
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Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle
Published 2025“…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. …”
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Adaptive temperature control of a reverse flow process by using reinforcement learning approach
Published 2024“…First, a policy iteration algorithm is introduced to learn the optimal solution of the associated linear-quadratic control problem online. …”
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DRL-Based IRS-Assisted Secure Visible Light Communications
Published 2022“…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. …”
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DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications
Published 2024“…To address this complexity optimally, we propose a deep reinforcement learning (DRL) approach based on the deep deterministic policy gradient (DDPG) technique. …”
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Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain
Published 2025“…<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. …”
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Uplink Noma in UAV-Assisted IoT Networks
Published 2022“…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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masterThesis
