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Articles 1501 - 1530 of 13783

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Planning Modeling And Optimization Algorithm For 5g Indoor Distribution System, Shaoda Zeng, Hailin Liu Mar 2024

Planning Modeling And Optimization Algorithm For 5g Indoor Distribution System, Shaoda Zeng, Hailin Liu

Journal of System Simulation

Abstract: Most of the new services in 5G mobile communication technologies, including smart homes, smart factories, and virtual reality, take place in indoor scenes. Therefore, how to quickly plan and build a 5G indoor distribution system with low construction cost and low power loss is of great significance for telecom operators. This paper establishes a mathematical planning model of a 5G indoor distribution system, which is closer to the actual scenario. The model aims to minimize the deployment cost and the maximum output signal power deviation between antennas, and the constraint is to meet the expected output signal power of …

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Simulation And Optimization Of Permanent Magnet Linear Machine Based On Deep Neural Network, Yan Shiliang, Yinling Wang, Dandan Lu, Xiaoqin Pan Mar 2024

Simulation And Optimization Of Permanent Magnet Linear Machine Based On Deep Neural Network, Yan Shiliang, Yinling Wang, Dandan Lu, Xiaoqin Pan

Journal of System Simulation

Abstract: The finite element model (FEM) of permanent magnet linear synchronous machines (PMLSMs) takes a long computing time and cannot directly display the relationship between structural parameters and output thrust, thus failing to guide the structural parameter optimization of the machine. An improved simulation model of PMLSMs based on the subdomain analytical method and deep neural network (DNN) algorithm is proposed. The magnetic flux density, no-load counter electromotive force (EMF), and other data are obtained according to Maxwell's equations. The nonlinear relationship between the structural parameters of the machine and output thrust is fitted by the DNN algorithm. Based on …

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Formation Strategy Of Hybrid Obstacle Avoidance Algorithm For Multiple Mobile Robots, Fulin Liu, Qingxin Li Mar 2024

Formation Strategy Of Hybrid Obstacle Avoidance Algorithm For Multiple Mobile Robots, Fulin Liu, Qingxin Li

Journal of System Simulation

Abstract: For the obstacle avoidance problem of multiple mobile robots in the unknown static obstacle environment, this paper proposed a formation strategy of a hybrid obstacle avoidance algorithm for multiple mobile robots, ensuring that multiple mobile robots do not collide during operation, can maintain the formation to the maximum extent in the unknown static obstacle environment for effective obstacle avoidance, and can reach the designated target point in a short time. Based on the leaderfollower method and artificial potential field (APF) method, the formation strategy divided the robots in the system into the leader robot and the follower robot. According …

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Research On Hybrid Experimental Scheme Design For Combat Simulation, Fei Liu, Peng Lai, Yingbo Lu, Min Wang, Zhifeng Lu Mar 2024

Research On Hybrid Experimental Scheme Design For Combat Simulation, Fei Liu, Peng Lai, Yingbo Lu, Min Wang, Zhifeng Lu

Journal of System Simulation

Abstract: Combat simulation experimental design refers to sampling the values of experimental factors based on baseline combat scenarios using various experimental design methods and then generating a set of experimental schemes for the sequential simulation. The complexity of combat simulation, such as numerous experimental factors and distinct factor types, including continuous and discrete numeric types, poses several challenges and requires efficient hybrid experimental design methods. To address these issues, this paper conducts a study on the hybrid experimental scheme design for combat simulation. This paper gives a brief classification and review of experimental design methods and presents three hybrid experimental …

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Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li Mar 2024

Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li

Journal of System Simulation

Abstract: Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the …

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Path Planning For Improvement Of A* Algorithm And Artificial Potential Field Method, Xiang Yu, Chen Jiang, Sirui Duan, Qianrui Deng Mar 2024

Path Planning For Improvement Of A* Algorithm And Artificial Potential Field Method, Xiang Yu, Chen Jiang, Sirui Duan, Qianrui Deng

Journal of System Simulation

Abstract: A* algorithm has the problem of too many polyline paths and search nodes, while the artificial potential field (APF) method has the problems of local optimality and unattainability. These problems are investigated in this paper. A new hybrid heuristic function is proposed based on the Euclidean distance and projection distance, based on which the A* algorithm process is improved accordingly. The search nodes of the A* algorithm are reduced, and the search efficiency is improved. The optimal node generated by the new A* algorithm is used as the local target point of the APF algorithm to assist in getting …

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Effectiveness Evaluation Of Heterogeneous Uav Swarms Based On A Hybrid Model, Yuanjie Lu, Shanshan Long, Hang Zhao, Guoxu Feng, Xiaojia Zhao Mar 2024

Effectiveness Evaluation Of Heterogeneous Uav Swarms Based On A Hybrid Model, Yuanjie Lu, Shanshan Long, Hang Zhao, Guoxu Feng, Xiaojia Zhao

Journal of System Simulation

Abstract: This paper presents a hybrid model based on availability dependability capability (ADC) system performance evaluation and back propagation (BP) neural network prediction to realize a rapid performance evaluation of UAV swarms and cope with the diversity of UAV swarm configuration and state and the complexity of performance calculation. By analyzing the components of swarm performance, a capability index system including the general platform capability, system-level capability, and task execution capability of UAVs is established. By using the ADC method, a swarm combat performance sample set is generated, and the BP neural network is used to construct a comprehensive combat …

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Construction Of Surrogate Model Driven By Model And Data, Jing An, Guangya Si, Miaoting Zeng Mar 2024

Construction Of Surrogate Model Driven By Model And Data, Jing An, Guangya Si, Miaoting Zeng

Journal of System Simulation

Abstract: By taking the three-dimensional projection action in a certain combat style as the research object, a surrogate model construction method driven by model and data is proposed to support the operational action research, so as to solve the problem that the calculation factors are too much during simulated deduction; the calculation resource cost is too large, and the calculation accuracy of the general analytical model is insufficient. Firstly, an analytical model group of three-dimensional projections with coefficients to be optimized is constructed based on military theory, including weapons and equipment, forces, etc. In addition, the composition and parameter setting …

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Laboratory Evaluation Of High-Temperature Resistant Lysine-Based Polymer Gel Systems For Leakage Control, Tao Song, Xuyang Tian, Baojun Bai, Yugandhara Eriyagama, Mohamed Ahdaya, Adel Alotibi, Thomas P. Schuman Mar 2024

Laboratory Evaluation Of High-Temperature Resistant Lysine-Based Polymer Gel Systems For Leakage Control, Tao Song, Xuyang Tian, Baojun Bai, Yugandhara Eriyagama, Mohamed Ahdaya, Adel Alotibi, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

In-situ crosslinking gel known for its cost-effectiveness, has been employed for decades to plug high-permeability features in subsurface environments. However, some commonly used crosslinkers are being phased out due to the increasingly rigorous environmental regulations. As a newly discovered environmentally friendly crosslinker, lysine can crosslink the partially hydrolyzed polyacrylamide through transamidation reaction. The present work aimed to study the effect of polymer composition and concentration on the gelation behavior of lysine and high molecular weight acrylamide-based polymers. Several commercial high molecular weight polymers with different contents of 2-Acrylamido-2-methyl-1-propane sulfonic acid (AMPS) including AN-105/125, SAV-55/37/28, and SAV-10 were deployed in this …

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Designing Explainable Ai To Improve Human-Ai Team Performance: A Medical Stakeholder-Driven Scoping Review, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank Mar 2024

Designing Explainable Ai To Improve Human-Ai Team Performance: A Medical Stakeholder-Driven Scoping Review, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank

Engineering Management and Systems Engineering Faculty Research & Creative Works

The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could …

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Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs Mar 2024

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

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Hybrid Features Extraction For The Online Mineral Grades Determination In The Flotation Froth Using Deep Learning, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Abderrahmane Benhayoun, Oumkeltoum Amar, François Bourzeix, Karim Baïna, Mouhamed Cherkaoui, Oussama Hasidi Mar 2024

Hybrid Features Extraction For The Online Mineral Grades Determination In The Flotation Froth Using Deep Learning, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Abderrahmane Benhayoun, Oumkeltoum Amar, François Bourzeix, Karim Baïna, Mouhamed Cherkaoui, Oussama Hasidi

Manufacturing & Industrial Engineering Faculty Publications

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. …

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The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang Mar 2024

The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang

Theses and Dissertations

Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …

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Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton Mar 2024

Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton

Theses and Dissertations

This research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in …

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An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel Mar 2024

An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel

Theses and Dissertations

The goal of this paper is to determine an optimal cycle length, in months, that minimizes costs and maximizes availability for deploying units in the United States (US) Army. The US Army must be cost efficient while maintaining the flexibility required to adapt to dynamic mission demand. The current practice is to deploy units for a length between the range of 6 to 12 months; however, this varies from unit to unit and the best policy is not clear. We address these issues by forming a mathematical programming model with unique characteristics that distinguish it from others of similar design. …

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Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig Mar 2024

Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig

Theses and Dissertations

As the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to …

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A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development, Kathleen A. Maclean Mar 2024

A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development, Kathleen A. Maclean

Theses and Dissertations

The integration of RL into wargames to learn strategic and operational insights is of interest to the United States Air Force. This thesis explores the application of a RL SARSA(λ) algorithm to the wargame Stratagem MIST. The primary objective is to select air and ground combat policies for the Blue Agent to effectively counter various opponent strategies across different terrains. This testing enables a comprehensive evaluation of the Blue Agent’s adaptability and performance under varying combat conditions. The use of basis functions, linear value function approximations, and specific air and ground strategies simplifies the state and action spaces of the …

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Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O Mar 2024

Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O

Theses and Dissertations

This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …

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Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi Mar 2024

Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi

Theses and Dissertations

This study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons …

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Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw Mar 2024

Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw

Theses and Dissertations

This research examines the operations of behavioral health clinics in Air Force Continental United States facilities, with a focus on operational efficiency and capacity through Data Envelopment Analysis. Most facilities operate near capacity, demonstrating efficient resource usage, but concerns arise with the potential of increased demand. Days-to-care and leakage are identified as major sources of inefficiency, suggesting that addressing these issues can enhance operations and reduce costs. The study also explores the impact of COVID-19 on care delivery.

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End-User Device Management In The Air And Space Forces, Joshua Bonvissuto Mar 2024

End-User Device Management In The Air And Space Forces, Joshua Bonvissuto

Theses and Dissertations

When achieving technical superiority is a matter a national security, it is critical that the Department of the Air Force (DAF) employs a robust Information Technology Asset Management (ITAM) strategy that maximizes mission effectiveness. Since 2022, the Office of the Chief Information Officer (SAF/CN) has been developing a plan to transform the Air Force’s ITAM strategy, primarily intending to centralize End User Device (EUD) procurement to the DAF level. For this study, a Delphi study was conducted to gather expert opinions on key aspects of SAF/CN’s new ITAM strategy to identify strengths, weaknesses, and implementation challenges. This research provides valuable …

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Human Performance Modeling Architecture With Hc-130j Mission Application, Stephanie G. Slimp Mar 2024

Human Performance Modeling Architecture With Hc-130j Mission Application, Stephanie G. Slimp

Theses and Dissertations

The DOD emphasizes digital engineering using Model-Based Systems Engineering (MBSE), where MBSE includes SysML-based models of systems. Analysis of system impacts on the human operators and their performance typically occurs through non-MBSE approaches, if at all. One current process, considered the As-Is process for this research, evaluates human performance and workload using IMPRINT, a discrete event simulation (DES) tool. IMPRINT primarily exists as a standalone tool with limited built-in functionality to integrate with an MBSE tool. Using the HC-130J and its crew during a CSAR mission as the system, this research develops a generalizable and updateable human performance modeling architecture …

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Rotorcraft Assisted Aircraft Inspection System: Creation And Component Assessment, Adam J. Warren Mar 2024

Rotorcraft Assisted Aircraft Inspection System: Creation And Component Assessment, Adam J. Warren

Theses and Dissertations

Aircraft require frequent inspections to perform their missions safely. Current visual inspection methods are time-consuming and dangerous for inspection personnel, but they are necessary to spot flaws that could endanger the aircraft during flight. This research explores a set of methods for transforming a set of target inspection criteria into camera specifications that will allow a UAS aircraft inspection system to perform inspections on the top skin of an aircraft. Given the minimum distance for the UAS to fly above the aircraft, minimum flaw size to search for on the aircraft, minimum number of pixels to display that flaw size, …

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Utilization Of The System Engineering Design Process To Design And Test A Low-Cost Infectious Aerosol Control Mechanism For Patient Aeromedical Evacuation, Sara Shaghaghi Mar 2024

Utilization Of The System Engineering Design Process To Design And Test A Low-Cost Infectious Aerosol Control Mechanism For Patient Aeromedical Evacuation, Sara Shaghaghi

Theses and Dissertations

The aeromedical evacuation of military patients is a critical component of care for Armed Forces members. The Air Force’s ability to transport patients relies on the technology and systems available. A vital transport responsibility is keeping the patient and medical personnel safe during transport. The historical and legacy systems provide reliable transport mechanisms for the Armed Forces’ patients infected with high-level biological agents, but drawbacks must be considered. This dissertation will discuss the development, conceptual design, and initial evaluation of a new low-cost, litter-mounted patient transport system, Biological-Mitigation in Patient Transport (B-MIPT), using the “V” model of the system engineering …

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Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink Mar 2024

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

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Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde Mar 2024

Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde

Theses and Dissertations

Current United States Department of Defense (DoD) standards require a minimum amount of parking for each building. This requirement defines how much off-street parking to construct. However, the impact of these requirements remains unclear. This study builds upon the emerging field of overhead imagery analytics by directly tying it to parking on military installations. Specifically, this study leverages a pretrained deep learning car detection model, Car Detection – USA, developed by Esri for use within ArcGIS, and couples it with open-access temporal imagery sourced from Google Earth Pro to assess selected parking lots across Area B, Wright-Patterson Air Force Base, …

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An Introduction Of Adaptive Training Aid Concepts And Its Application To Accelerated Training For Air Battle Managers, John C. Gillispie Mar 2024

An Introduction Of Adaptive Training Aid Concepts And Its Application To Accelerated Training For Air Battle Managers, John C. Gillispie

Theses and Dissertations

Over the years, the integration of artificial intelligence (AI) to enable autonomous systems has undergone transformative shifts in the Department of Defense (DoD), revolutionizing capabilities and strategic approaches. To further optimize these advancements, varying levels of autonomy have been introduced across critical military applications, spanning intelligence, surveillance, reconnaissance (ISR), air battle management, and offensive/defensive air operations. As the technological landscape expands, so do the opportunities for autonomy to augment operations through human-agent teaming. Within the Air Force, one notably cognitively demanding role that stands to benefit from these strides is that of the Air Battle Manager (ABM). In support of …

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Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri Mar 2024

Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri

Theses and Dissertations

This article addresses the optimization of a specialized deployment kit crucial for air force operations, emphasizing the need for rapid and efficient aircraft deployment. Through a comprehensive analysis of historical data, the study aims to streamline the kit's contents without compromising effectiveness. The research suggests a potential reduction of 7.8%, with an impressive 60.5% decrease if a minimal 5% threshold is deemed acceptable. While the primary focus is on air force deployment, the broader implications extend to military entities, such as armies and navies, highlighting the applicability of the findings in enhancing deployment efficiency across various defense sectors.

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Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham Mar 2024

Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham

Theses and Dissertations

Neural networks, despite their prowess in computer vision, often exhibit "flickering". Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network's confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness.

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A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae Mar 2024

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

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