Reinforcement Learning Technology Research Articles

A Multimodal Fusion and Ensemble Approach for Robust Fake News Detection through Deep Learning, Reinforcement Learning, and Using Blockchain to Enhance Security and Authorization

Background: Computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information. Objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. Methods: In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information. Results: Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication. Conclusion: Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed model performs better than multilayer perceptron (MLP), and random forest (RF).

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

Reinforcement learning based blockchain model for revoking unauthorized access in Virtualized Network Functions‐based Internet of Things Mobile Edge Computing

AbstractVNFs boost data processing efficiency in Mobile Edge Computing (MEC)‐driven Internet of Things (IoT) for healthcare, smart cities, and industrial automation. VNF‐based IoT MEC systems encounter a significant security threat due to unauthorized access, posing risks to data privacy and system integrity. Existing approaches struggle to adapt to dynamic environments and lack tamper‐proof enforcement mechanisms. In this work, we propose a novel system combining Reinforcement Learning (RL) and blockchain technology to revoke unauthorized access in VNF‐based IoT MEC. We introduce the Integrated Action‐selection DRL Algorithm for Unauthorized Access Revocation (IASDRL‐UAR), a novel RL approach that excels in dynamic environments by handling both continuous and discrete actions, enabling real‐time optimization of security risk, execution time, and energy consumption. A behavior control contract (BCC) is proposed and integrated into the RL system, automating behavior checks and enforcement, streamlining security management, and reducing manual intervention. RL feedback plays a pivotal role in steering dynamic security adjustments, gaining valuable perspectives from user behavior via trust scores in the behavior contract. The security features of the proposed method are analyzed. Performance comparisons reveal a substantial improvement, with the proposed system outperforming existing methods by 30% in terms of throughput, 21.7% in system stability, and 26% in access revocation latency. Additionally, the system demonstrates a higher security index, energy efficiency, and scalability.

Safe tracking in games: Achieving optimal control with unknown dynamics and constraints

AbstractThis paper introduces mix‐zero‐sum differential (MZSD) game theory to address multi‐player tracking systems, offering a better understanding of the coexistence of cooperation and competition among players. Within this framework, we present an optimal safety tracking control (OSTC) method, which incorporates a control barrier function (CBF) into the value function to ensure that the tracking error remains within a specified range, thus guaranteeing safety while achieving optimization. Simultaneously, to eliminate the need for system dynamics, we propose a novel approach leveraging off‐policy integral reinforcement learning (IRL) technology to obtain the Nash equilibrium solution of the MZSD games. We establish a unique critics–actors neural network (NN) structure that updates concurrently. Furthermore, we analyze stability and convergence using the Lyapunov method. We conduct two simulations to demonstrate the effectiveness of the proposed algorithm.

Piano harmony automatic adaptation system based on deep reinforcement learning

Piano harmony is a combination of multiple notes, which plays a vital role in enriching the expression of melody. However, research on the piano harmony automatic allocation system is insufficient, and the practical application of reinforcement learning technology is scarce. Based on this, this paper determines the key of the piano harmony automatic allocation system through the in-depth analysis of piano harmony, then arranges the piano harmony with the help of reinforcement learning rather than manual methods and designs a complete piano harmony allocation system it provides powerful help for piano music creation. Sufficient experiments show that the piano harmony automatic orchestration system based on reinforcement learning proposed in this paper has high accuracy, and the orchestration effect has a similarity of 93% with the creation of musicians, which the majority of piano scholars have recognized.

FAKE MEDIA DETECTION BASED ON SOCIAL MEDIA USING MACHINE LEARNING

The proposed solution for detecting false news involves a combination of Natural Language Processing (NLP) techniques, Reinforcement Learning (RL), and blockchain technology. The process begins with the collection of a comprehensive dataset of news articles and their associated metadata, followed by NLP-based pre-processing to clean and tokenize the text. Relevant features, such as word frequencies and readability, are then extracted and used to train an RL agent. The agent is trained to distinguish between true and false news using a reward and punishment system for learning. Once trained, the RL agent can classify new articles as true or false based on their extracted features. Although the potential role of blockchain technology is mentioned, further elaboration is required. This innovative approach is aimed at combating the dissemination of false information and misinformation in the digital news. Keywords: Natural Language Processing (NLP), Block chain, Fake Media

10.55041/ISJEM01502

The proposed solution for detecting false news involves a combination of Natural Language Processing (NLP) techniques, Reinforcement Learning (RL), and blockchain technology. The process begins with the collection of a comprehensive dataset of news articles and their associated metadata, followed by NLP-based pre-processing to clean and tokenize the text. Relevant features, such as word frequencies and readability, are then extracted and used to train an RL agent. The agent is trained to distinguish between true and false news using a reward and punishment system for learning. Once trained, the RL agent can classify new articles as true or false based on their extracted features. Although the potential role of blockchain technology is mentioned, further elaboration is required. This innovative approach is aimed at combating the dissemination of false information and misinformation in the digital news. Keywords: Natural Language Processing (NLP), Block chain, Fake Media

A generalisable tool path planning strategy for free-form sheet metal stamping through deep reinforcement and supervised learning

AbstractDue to the high cost of specially customised presses and dies and the advance of machine learning technology, there is some emerging research attempting free-form sheet metal stamping processes which use several common tools to produce products of various shapes. However, tool path planning strategies for the free forming process, such as reinforcement learning technique, derived from previous path planning experience are not generalisable for an arbitrary new sheet metal workpiece. Thus, in this paper, a generalisable tool path planning strategy is proposed for the first time to realise the tool path prediction for an arbitrary sheet metal part in 2-D space with no metal forming knowledge in prior, through deep reinforcement (implemented with 2 heuristics) and supervised learning technologies. Conferred by deep learning, the tool path planning process is corroborated to have self-learning characteristics. This method has been instantiated and verified by a successful application to a case study, of which the workpiece shape deformed by the predicted tool path has been compared with its target shape. The proposed method significantly improves the generalisation of tool path planning of free-form sheet metal stamping process, compared to strategies using pure reinforcement learning technologies. The successful instantiation of this method also implies the potential of the development of intelligent free-form sheet metal stamping process.

Dynamic resource allocation for virtual machine migration optimization using machine learning

This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

Design optimization of obstacle avoidance of intelligent building the steel bar by integrating reinforcement learning and BIM technology

In promoting the construction of prefabricated residential buildings in Yunnan villages and towns, the use of precast concrete elements is unstoppable. Due to the dense arrangement of steel bars at the joints of precast concrete elements, collisions are prone to occur, which can affect the stress of the components and even pose certain safety hazards for the entire construction project. Because the commonly used the steel bar obstacle avoidance method based on building information modeling has low adaptation rate and cannot change the trajectory of the steel bar to avoid collision, a multi-agent reinforcement learning-based model integrating building information modeling is proposed to solve the steel bar collision in reinforced concrete frame. The experimental results show that the probability of obstacle avoidance of the proposed model in three typical beam-column joints is 98.45%, 98.62% and 98.39% respectively, which is 5.16%, 12.81% and 17.50% higher than that of the building information modeling. In the collision-free path design of the same object, the research on the path design of different types of precast concrete elements takes about 3–4 minutes, which is far less than the time spent by experienced structural engineers on collision-free path modeling. The experimental results indicate that the model constructed by the research institute has good performance and has certain reference significance.

The application of reinforcement learning algorithms in intelligent games

The ongoing advancement of computer hardware has spurred the need for high-quality gaming experiences. The pressing need for intelligent games, the proven efficacy of reinforcement learning in various fields, the advancement of deep reinforcement learning technology, and the desire for customized gaming experiences collectively propel the ongoing exploration and implementation of reinforcement learning in intelligent games.This study use algorithms sourced from the literature review to examine multiple facets of reinforcement learning, including the modeling of game settings, reward design, the utilization of deep learning approaches, and adversarial learning. The utilization of reinforcement learning in intelligent games holds immense importance. Initially, it enhances the gaming encounter and renders intelligent games more demanding and tailored. Furthermore, the game gains complexity and intrigue by the implementation of intelligent adversaries capable of assimilating and adjusting to the player's tactics. This not only fosters the advancement of AI technology through research and development, but also broadens the utilization of reinforcement learning in other domains, like autonomous driving and smart manufacturing. Furthermore, tailored encounters, cutting-edge game design, and utilization in schooling are likewise significant contributions, rendering reinforcement learning a pivotal element in propelling technological advancement and augmenting user experience.

Application of Exercise Recommendation Model Based on Deep Reinforcement Learning

In order to consolidate the learning achievements of students at a certain stage, Teachers often provide students with corresponding exercises before or after the beginning of this stage. Therefore, making the exercises appropriately challenging is one of the main goals of adaptive online learning systems. However, because each student's learning status is different, a student's learning situation in the same period of time will also be different. Therefore, it is also very challenging for students to choose appropriate exercises. In this paper, we propose a new method for problem recommendation. Firstly, we use GCKT model to model the user's answer sequence. Obtain the students' mastery of each concept. The learned user and problem representation are integrated into an extended framework to predict the likelihood of user mastery of the problem. Then use this as the basis for recommending exercises. On this basis, the deep reinforcement learning technology is used and the knowledge tracking model is used as a student simulator. The difference in the performance of the student simulator on all the exercises before and after solving the exercises provided by the exercise recommendation model is used as a reward, Make the model learn what kind of exercises can improve students' ability to the greatest extent, and recommend such exercises to students. Finally, the experiment is carried out in the actual use environment. The results show that the model has better performance than the current common recommendation models.

Intelligent Exploration of Environmental Design: Combining CAD Modeling and Reinforcement Learning Technology

Intelligent Exploration of Environmental Design: Combining CAD Modeling and Reinforcement Learning Technology

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections.

Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated Shorts.

Semantic communication technology in the 6G wireless system focuses on semantic extraction in communication, that is, only the inherent meaning of the intention in the information. Existing technologies still have challenges in extracting emotional perception in the information, high compression rates, and privacy leakage due to knowledge sharing in communication. Large-scale generative-model technology could rapidly generate multimodal information according to user requirements. This paper proposes an approach that leverages large-scale generative models to create animated short films that are semantically and emotionally similar to real scenes and characters. The visual content of the data source is converted into text expression through semantic understanding technology; emotional clues from the data source media are added to the text form through reinforcement learning technology; and finally, a large-scale generative model is used to generate visual media, which is consistent with the semantics of the data source. This paper develops a semantic communication process with distinct modules and assesses the enhancements garnered from incorporating an emotion enhancement module. This approach facilitates the expedited generation of broad media forms and volumes according to the user's intention, thereby enabling the creation of generated multimodal media within applications in the metaverse and in intelligent driving systems.

Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment Monitoring.

As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. However, the sensing environment is often so complex that the observable and unobservable data collected are sparse and heterogeneous, affecting the accuracy of the reconstruction. In this paper, we focus on developing a robust multi-agent environment monitoring framework, called self-interested coalitional crowdsensing for multi-agent interactive environment monitoring (SCC-MIE), including environment reconstruction and worker selection. In SCC-MIE, we start from a multi-agent generative adversarial imitation learning framework to introduce a new self-interested coalitional learning strategy, which forges cooperation between a reconstructor and a discriminator to learn the sensing environment together with the hidden confounder while providing interpretability on the results of environment monitoring. Based on this, we utilize the secretary problem to select suitable workers to collect data for accurate environment monitoring in a real-time manner. It is shown that SCC-MIE realizes a significant performance improvement in environment monitoring compared to the existing models.

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

With the development and maturity of the Internet education industry, more and more vocational colleges have opened English teaching courses based on massive open online courses courses. However, there are many English-related courses on the massive open online courses course platform, and the use of scientific recommendation models can improve the teaching quality of such courses. Therefore, this research attempts to design two improved attention mechanisms and user-based embedded expression using meta-path technology. At the same time, these two are combined with reinforcement learning technology to design an improved massive open online courses English course recommendation model. The test results show that the hit rate of the model designed in this study is 89.84%, 74.28%, 70.81% and 71.35% respectively when the rank number is 20 and the parameter is 10. At this time, the cumulative income of normalized discount is 48.24%, 34.58%, 25.96% and 28.69% respectively. However, when the number of calculated samples reaches the maximum value of 1158609, the calculation time of the improved reinforcement learning recommendation model is 1867 seconds, which is also higher than the comparison model. The experimental results show that the curriculum recommendation accuracy of the massive open online courses recommendation model designed in this study is higher and the recommendation results are more reasonable. The results of this research have a certain application potential in the field of the construction of online education in colleges and universities.

Deep reinforcement learning in agricultural IoT-based: A review

The world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implemented, especially in the field of IoT-based agriculture. The research method uses a Systematic Literature Review (SLR) approach through searching and analysing raw data sources, sorting and selecting relevant data relevant to the topics discussed, discussing topic areas and how trends are in current conditions, and concluding. The purpose of this study is to see how the current state of DRL implementation in agricultural IoT-based. The limitations of the study are that (1) the data sources come from Scopus-indexed journals; (2) the journal period is 2021–2023; (3) the research approach uses SLR; and (4) the focus of the discussion includes the implementation of DRL in agricultural IoT-based systems, the development of DRL technology, and the use of tools in DRL.

Combining Software-Defined and Delay-Tolerant Networking Concepts with Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Combining Software-Defined and Delay-Tolerant Networking Concepts with Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Memory-Enhanced Twin Delayed Deep Deterministic Policy Gradient (ME-TD3)-Based Unmanned Combat Aerial Vehicle Trajectory Planning for Avoiding Radar Detection Threats in Dynamic and Unknown Environments

Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars exacerbate the complexity of this problem. In this paper, we propose a novel UCAV trajectory planning method based on deep reinforcement learning (DRL) technology to overcome the adverse impacts caused by the dynamics and randomness of environments. A predictive control model is constructed to describe the dynamic characteristics of the UCAV trajectory planning problem in detail. To improve the UCAV’s predictive ability, we propose a memory-enhanced twin delayed deep deterministic policy gradient (ME-TD3) algorithm that uses an attention mechanism to effectively extract environmental patterns from historical information. The simulation results show that the proposed method can successfully train UCAVs to carry out trajectory planning tasks in dynamic and unknown environments. Furthermore, the ME-TD3 algorithm outperforms other classical DRL algorithms in UCAV trajectory planning, exhibiting superior performance and adaptability.