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Articoli di riviste sul tema "Reinforcement learning (Machine learning)"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 7 settembre 2023

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1

Ishii, Shin, and Wako Yoshida. "Part 4: Reinforcement learning: Machine learning and natural learning." New Generation Computing 24, no. 3 (September 2006): 325–50. http://dx.doi.org/10.1007/bf03037338.

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Wang, Zizhuang. "Temporal-Related Convolutional-Restricted-Boltzmann-Machine Capable of Learning Relational Order via Reinforcement Learning Procedure." International Journal of Machine Learning and Computing 7, no. 1 (February 2017): 1–8. http://dx.doi.org/10.18178/ijmlc.2017.7.1.610.

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Butlin, Patrick. "Machine Learning, Functions and Goals." Croatian journal of philosophy 22, no. 66 (December 27, 2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.

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Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to

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Martín-Guerrero, José D., and Lucas Lamata. "Reinforcement Learning and Physics." Applied Sciences 11, no. 18 (September 16, 2021): 8589. http://dx.doi.org/10.3390/app11188589.

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Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of

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Liu, Yicen, Yu Lu, Xi Li, Wenxin Qiao, Zhiwei Li, and Donghao Zhao. "SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches." IEEE Communications Letters 25, no. 6 (June 2021): 1926–30. http://dx.doi.org/10.1109/lcomm.2021.3061991.

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Popkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning." Mathematics 11, no. 17 (August 23, 2023): 3651. http://dx.doi.org/10.3390/math11173651.

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This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm

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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (February 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations

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Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum c

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Sahu, Santosh Kumar, Anil Mokhade, and Neeraj Dhanraj Bokde. "An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges." Applied Sciences 13, no. 3 (February 2, 2023): 1956. http://dx.doi.org/10.3390/app13031956.

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Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate

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Fang, Qiang, Wenzhuo Zhang, and Xitong Wang. "Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine." Electronics 10, no. 16 (August 18, 2021): 1997. http://dx.doi.org/10.3390/electronics10161997.

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In this paper, we focus on the challenges of training efficiency, the designation of reward functions, and generalization in reinforcement learning for visual navigation and propose a regularized extreme learning machine-based inverse reinforcement learning approach (RELM-IRL) to improve the navigation performance. Our contributions are mainly three-fold: First, a framework combining extreme learning machine with inverse reinforcement learning is presented. This framework can improve the sample efficiency and obtain the reward function directly from the image information observed by the agent

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Sarikhani, Rahil, and Farshid Keynia. "Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach." IEEE Communications Letters 24, no. 7 (July 2020): 1459–62. http://dx.doi.org/10.1109/lcomm.2020.2984430.

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AlDahoul, Nouar, Zaw Zaw Htike, and Rini Akmeliawati. "Hierarchical extreme learning machine based reinforcement learning for goal localization." IOP Conference Series: Materials Science and Engineering 184 (March 2017): 012055. http://dx.doi.org/10.1088/1757-899x/184/1/012055.

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McPartland, Michelle, and Marcus Gallagher. "Learning to be a Bot: Reinforcement Learning in Shooter Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4, no. 1 (September 27, 2021): 78–83. http://dx.doi.org/10.1609/aiide.v4i1.18676.

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This paper demonstrates the applicability of reinforcement learning for first person shooter bot artificial intelligence. Reinforcement learning is a machine learning technique where an agent learns a problem through interaction with the environment. The Sarsa(&lambda;) algorithm will be applied to a first person shooter bot controller to learn the tasks of (1) navigation and item collection, and (2) combat. The results will show the validity and diversity of reinforcement learning in a first person shooter environment.

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Zine, Mohamed, Fouzi Harrou, Mohammed Terbeche, Mohammed Bellahcene, Abdelkader Dairi, and Ying Sun. "E-Learning Readiness Assessment Using Machine Learning Methods." Sustainability 15, no. 11 (June 1, 2023): 8924. http://dx.doi.org/10.3390/su15118924.

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Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tle

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Zhang, Ziyu. "Basic things about reinforcement learning." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 280–84. http://dx.doi.org/10.54254/2755-2721/6/20230788.

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Artificial Intelligence has been a very popular topic at present, machine learning is also one of the main algorithms in AI, which consisted of Supervised learning, Unsupervised learning and Reinforcement learning, and Supervised learning and Unsupervised learning have been relatively mature. Reinforcement learning technology has a long history, it wasn't until the late '80s and early' 90s that reinforcement learning became widely used in artificial intelligence, machine learning. Generally, Reinforcement learning is a process of trial and error, agent will choose to make an action according t

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Yang, Yanxiang, Jiang Hu, Dana Porter, Thomas Marek, Kevin Heflin, and Hongxin Kong. "Deep Reinforcement Learning-Based Irrigation Scheduling." Transactions of the ASABE 63, no. 3 (2020): 549–56. http://dx.doi.org/10.13031/trans.13633.

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Highlights Deep reinforcement learning-based irrigation scheduling is proposed to determine the amount of irrigation required at each time step considering soil moisture level, evapotranspiration, forecast precipitation, and crop growth stage. The proposed methodology was compared with traditional irrigation scheduling approaches and some machine learning based scheduling approaches based on simulation. Abstract. Machine learning has been widely applied in many areas, with promising results and large potential. In this article, deep reinforcement learning-based irrigation scheduling is propose

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DiGiovanna, J., B. Mahmoudi, J. Fortes, J. C. Principe, and J. C. Sanchez. "Coadaptive Brain–Machine Interface via Reinforcement Learning." IEEE Transactions on Biomedical Engineering 56, no. 1 (January 2009): 54–64. http://dx.doi.org/10.1109/tbme.2008.926699.

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NAKATANI, Masayuki, Zeyuan SUN, and Yutaka UCHIMURA. "Intelligent Construction Machine by Deep Reinforcement Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—G03. http://dx.doi.org/10.1299/jsmermd.2017.2p2-g03.

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Mahdi, Hiba. "Blockchain and Machine Learning as Deep Reinforcement." Wasit Journal of Computer and Mathematics Science 2, no. 1 (March 31, 2023): 72–84. http://dx.doi.org/10.31185/wjcm.103.

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Due to its capacity to make wise decisions, deep learning has become extremely popular in recent years. The current generation of deep learning, which heavily rely centralized servers, are unable to offer attributes like operational transparency, stability, security, and reliable data provenance. Additionally, Single point of failure is a problem that deep learning designs are susceptible since they need centralized data to train them. We review the body of research on the application of deep learning to blockchain. We categorize and arrange the literature for developing topic taxonomy based t

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Hoshino, Yukinobu, and Katsuari Kamei. "Effective Use of Learning Knowledge by FEERL." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 1 (February 20, 2003): 6–9. http://dx.doi.org/10.20965/jaciii.2003.p0006.

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The machine learning is proposed to learning techniques of spcialists. A machine has to learn techniques by trial and error when there are no training examples. Reinforcement learning is a powerful machine learning system, which is able to learn without giving training examples to a learning unit. But it is impossible for the reinforcement learning to support large environments because the number of if-then rules is a huge combination of a relationship between one environment and one action. We have proposed new reinforcement learning system for the large environment, Fuzzy Environment Evaluat

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Jadhav, Rutuja. "Tracking Locomotion using Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1777–83. http://dx.doi.org/10.22214/ijraset.2022.45509.

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Abstract: This article presents the concept of reinforcement learning, which prepares a static direct approach for consistent control problems, and adjusts cutting-edge techniques for testing effectiveness in benchmark Mujoco locomotion tasks. This model was designed and developed to use the Mujoco Engine to track the movement of robotic structures and eliminate problems with assessment calculations using perceptron’s and random search algorithms. Here, the machine learning model is trained to make a series of decisions. The humanoid model is considered to be one of the most difficult and ongo

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Eckardt, Jan-Niklas, Karsten Wendt, Martin Bornhäuser, and Jan Moritz Middeke. "Reinforcement Learning for Precision Oncology." Cancers 13, no. 18 (September 15, 2021): 4624. http://dx.doi.org/10.3390/cancers13184624.

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Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order t

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Kaelbling, L. P., M. L. Littman, and A. W. Moore. "Reinforcement Learning: A Survey." Journal of Artificial Intelligence Research 4 (May 1, 1996): 237–85. http://dx.doi.org/10.1613/jair.301.

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This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinfo

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Tizhoosh, Hamid R. "Opposition-Based Reinforcement Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 4 (July 20, 2006): 578–85. http://dx.doi.org/10.20965/jaciii.2006.p0578.

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Reinforcement learning is a machine intelligence scheme for learning in highly dynamic, probabilistic environments. By interaction with the environment, reinforcement agents learn optimal control policies, especially in the absence of a priori knowledge and/or a sufficiently large amount of training data. Despite its advantages, however, reinforcement learning suffers from a major drawback - high calculation cost because convergence to an optimal solution usually requires that all states be visited frequently to ensure that policy is reliable. This is not always possible, however, due to the c

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Meng, Terry Lingze, and Matloob Khushi. "Reinforcement Learning in Financial Markets." Data 4, no. 3 (July 28, 2019): 110. http://dx.doi.org/10.3390/data4030110.

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Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and

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Senthil, Chandran, and Ranjitharamasamy Sudhakara Pandian. "Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry." Processes 10, no. 2 (February 14, 2022): 371. http://dx.doi.org/10.3390/pr10020371.

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This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would inter

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Saeed, Shaheer U., Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, et al. "Image quality assessment for machine learning tasks using meta-reinforcement learning." Medical Image Analysis 78 (May 2022): 102427. http://dx.doi.org/10.1016/j.media.2022.102427.

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AlDahoul, Nouar, and ZawZaw Htike. "Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting." International Journal of ADVANCED AND APPLIED SCIENCES 6, no. 1 (January 2019): 106–13. http://dx.doi.org/10.21833/ijaas.2019.01.015.

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Calabuig, J. M., H. Falciani, and E. A. Sánchez-Pérez. "Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets." Neurocomputing 398 (July 2020): 172–84. http://dx.doi.org/10.1016/j.neucom.2020.02.052.

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Zhang, Junhao, and Yifei Lei. "Deep Reinforcement Learning for Stock Prediction." Scientific Programming 2022 (April 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5812546.

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Investors are frequently concerned with the potential return from changes in a company’s stock price. However, stock price fluctuations are frequently highly nonlinear and nonstationary, rendering them to be uncontrollable and the primary reason why the majority of investors earn low long-term returns. Historically, people have always simulated and predicted using classic econometric models and simple machine learning models. In recent years, an increasing amount of research has been conducted using more complex machine learning and deep learning methods to forecast stock prices, and their res

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Xu, Zhe, Ivan Gavran, Yousef Ahmad, Rupak Majumdar, Daniel Neider, Ufuk Topcu, and Bo Wu. "Joint Inference of Reward Machines and Policies for Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 590–98. http://dx.doi.org/10.1609/icaps.v30i1.6756.

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Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where the high-level knowledge is in the form of reward machines, a type of Mealy machines that encode non-Markovian reward functions. We focus on a setting in which this knowledge is a priori not available to the learning agent. We develop an iterative algorithm that performs joint inference of reward machines and policies for RL (more specifically, q-learning). In each iteration, the algorithm maintains a hypothesis rew

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Koker, Thomas E., and Dimitrios Koutmos. "Cryptocurrency Trading Using Machine Learning." Journal of Risk and Financial Management 13, no. 8 (August 10, 2020): 178. http://dx.doi.org/10.3390/jrfm13080178.

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We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.

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Chen, Irene Y., Shalmali Joshi, Marzyeh Ghassemi, and Rajesh Ranganath. "Probabilistic Machine Learning for Healthcare." Annual Review of Biomedical Data Science 4, no. 1 (July 20, 2021): 393–415. http://dx.doi.org/10.1146/annurev-biodatasci-092820-033938.

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Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

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Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is m

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Shailaja, Dr M., Nune Vinaya Reddy, Ambati Srujani, and Cherukuthota Upeksha Reddy. "Playing Tetris with Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2088–95. http://dx.doi.org/10.22214/ijraset.2022.44208.

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Abstract: The essential inspiration for this uAndertaking was a pleasant utilization of AI. Tetris is a notable game that is cherished and loathed by a lot of people. Tetris game has a few qualitiesmaking it an intriguing issue for the field of ML. A total portrayal of the tetris issue incorporates tremendous number of states making a meaning of a non-learning procedure for all intents and purposes unthinkable. Late outcomes from the group at Google DeepMind have shown that support learning can have noteworthy execution at game playing, utilizing a negligible measure of earlier data about the

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Dandoti, Sarosh. "Learning to Survive using Reinforcement Learning with MLAgents." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3009–14. http://dx.doi.org/10.22214/ijraset.2022.45526.

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Abstract: Simulations have been there for a long time, in different versions and level of complexity. Training a Reinforcement Learning model in a 3D environment lets us understand a lot of new insights from the inference. There have been some examples where the AI learns to Feed Itself, Learns to Start walking, jumping etc. The reason one trains an entire model from the agent knowing nothing to being a perfect task achiever is that during the process, new behavioral patterns can be recorded. Reinforcement Learning is a feedback-based Machine Learning technique in which an agent learns how to

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Lockwood, Owen, and Mei Si. "Reinforcement Learning with Quantum Variational Circuit." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.

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The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present

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Meera, A., and S. Swamynathan. "Queue Based Q-Learning for Efficient Resource Provisioning in Cloud Data Centers." International Journal of Intelligent Information Technologies 11, no. 4 (October 2015): 37–54. http://dx.doi.org/10.4018/ijiit.2015100103.

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Cloud Computing is a novel paradigm that offers virtual resources on demand through internet. Due to rapid demand to cloud resources, it is difficult to estimate the user's demand. As a result, the complexity of resource provisioning increases, which leads to the requirement of an adaptive resource provisioning. In this paper, the authors address the problem of efficient resource provisioning through Queue based Q-learning algorithm using reinforcement learning agent. Reinforcement learning has been proved in various domains for automatic control and resource provisioning. In the absence of co

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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (September 1, 2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. Th

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Mondal, Shanka Subhra, Nikhil Sheoran, and Subrata Mitra. "Scheduling of Time-Varying Workloads Using Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9000–9008. http://dx.doi.org/10.1609/aaai.v35i10.17088.

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Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource usage between two workloads running on the same machine can create performance degradation, unused resources in a machine results in wastage and undesirable operational characteristics for a compute cluster. Prior works did not consider such temporal resource fluctuations or their alignment for scheduling decisions. Due to the variety of time-varying workloads, their complex resource usage characteristics, it is challenging to design well

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Lürig, Christoph. "Learning Machine Learning with a Game." European Conference on Games Based Learning 16, no. 1 (September 29, 2022): 316–23. http://dx.doi.org/10.34190/ecgbl.16.1.481.

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AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard to crack problem for AI for a long time. Additionally, we see the rise of explainable AI (xAI), which tries to address the problem that many modern AI decision techniques are black-box approaches and incomprehensible to humans. Combining a board game AI for the relatively simple game Connect-Four with explanation techniques offers the possibility of learning something about an AI's inner

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Shaveta. "A review on machine learning." International Journal of Science and Research Archive 9, no. 1 (May 30, 2023): 281–85. http://dx.doi.org/10.30574/ijsra.2023.9.1.0410.

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Machine learning is a particular branch of artificial intelligence that teaches a machine how to learn, whereas artificial intelligence (AI) is the general science that aims to emulate human abilities. An AI method called machine learning teaches computers to learn from their past experiences. Machine learning algorithms don't rely on a predetermined equation as a model, but instead "learn" information directly from data using computational techniques. As the quantity of learning examples increases, the algorithms adaptively get better at what they do. This paper provides an overview of the fi

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Wang, Canjun, Zhao Li, Tong Chen, Ruishuang Wang, and Zhengyu Ju. "Research on the Application of Prompt Learning Pretrained Language Model in Machine Translation Task with Reinforcement Learning." Electronics 12, no. 16 (August 9, 2023): 3391. http://dx.doi.org/10.3390/electronics12163391.

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With the continuous advancement of deep learning technology, pretrained language models have emerged as crucial tools for natural language processing tasks. However, optimization of pretrained language models is essential for specific tasks such as machine translation. This paper presents a novel approach that integrates reinforcement learning with prompt learning to enhance the performance of pretrained language models in machine translation tasks. In our methodology, a “prompt” string is incorporated into the input of the pretrained language model, to guide the generation of an output that a

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Belozerov, Ilya Andreevich, and Vladimir Anatolievich Sudakov. "Reinforcement Machine Learning for Solving Mathematical Programming Problems." Keldysh Institute Preprints, no. 36 (2022): 1–14. http://dx.doi.org/10.20948/prepr-2022-36.

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This paper discusses modern approaches to finding rational solutions in problems of mixed integer linear programming, both generated with random data and from real practice. The main emphasis is on how to implement the process of finding a solution to discrete optimization problems using the concept of reinforcement learning; what techniques can be applied to improve the speed and quality of work. Three main variants of the algorithm were developed using the Ray library API, as well as the environment - the Gym library. The results of the developed solver are compared with the OR-Tools library

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Cappart, Quentin, Emmanuel Goutierre, David Bergman, and Louis-Martin Rousseau. "Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1443–51. http://dx.doi.org/10.1609/aaai.v33i01.33011443.

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Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In th

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R. Merina, Queentin. "Use of reinforcement learning algorithms to optimize control strategies for single machine systems." i-manager’s Journal on Instrumentation and Control Engineering 10, no. 2 (2022): 36. http://dx.doi.org/10.26634/jic.10.2.19357.

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The stability of power systems is critical to ensuring reliable and efficient operation of electrical grids. In recent years, there has been a growing interest in the use of artificial intelligence techniques, such as reinforcement learning, to improve the stability of single machine systems. Reinforcement learning is a machine learning approach that enables agents to learn optimal control policies through trial and error. In this paper, we explore the use of reinforcement learning algorithms to optimize control strategies for single machine systems. We demonstrate how these algorithms can be

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Elizarov, Artem Aleksandrovich, and Evgenii Viktorovich Razinkov. "Image Classification Using Reinforcement Learning." Russian Digital Libraries Journal 23, no. 6 (May 12, 2020): 1172–91. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1172-1191.

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Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence. The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of the developed method comes down to solving the problem of a contextual multi-armed

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Jiang, Weihang. "Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 473–78. http://dx.doi.org/10.54254/2755-2721/4/2023308.

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Abstract (sommario):

High-performance machine learning algorithms have always been one of the concerns of many researchers. Since its birth, machine learning has been a product of multidisciplinary integration. Especially in the field of neuroscience, models from related fields continue to inspire the development of neural networks and deepen people's understanding of neural networks. The mathematical and quantitative modeling approach to research brought about by machine learning is also feeding into the development of neuroscience. One of the emerging products of this is computational neuroscience. Computational

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Kong, Xiang, Zhaopeng Tu, Shuming Shi, Eduard Hovy, and Tong Zhang. "Neural Machine Translation with Adequacy-Oriented Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6618–25. http://dx.doi.org/10.1609/aaai.v33i01.33016618.

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Abstract (sommario):

Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate translation. We attribute this to that the standard Maximum Likelihood Estimation (MLE) cannot judge the real translation quality due to its several limitations. In this work, we propose an adequacyoriented learning mechanism for NMT by casting translation as a stochastic policy in Reinforcement Learning (RL), where the reward is estimated by explicitly measuring translation adequacy. Benefiting from the sequence-level training of RL strateg

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Durgut, Rafet, Mehmet Emin Aydin, and Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach." Algorithms 15, no. 1 (January 17, 2022): 24. http://dx.doi.org/10.3390/a15010024.

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Abstract (sommario):

In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to

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