Search Results - Computing methodologies Machine learning Machine learning approaches Markov decision processes

Exploring reinforcement learning in process control: a comprehensive survey.

Authors: Rajasekhar, N. Radhakrishnan, T.K. Samsudeen, N.

Source: International Journal of Systems Science; Oct2025, Vol. 56 Issue 14, p3528-3557, 30p

Subject Terms: REINFORCEMENT learning, PROCESS control systems, MACHINE learning, DECISION making, ALGORITHMS, DEEP learning, INDUSTRIAL applications

Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control.

Authors: Gutiérrez-Moreno, Rodrigo Barea, Rafael López-Guillén, Elena et al.

Source: Sensors (14248220); Jan2025, Vol. 25 Issue 1, p117, 20p

Subject Terms: REINFORCEMENT learning, DEEP reinforcement learning, PARTIALLY observable Markov decision processes, MACHINE learning, DEEP learning

Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing.

Authors: Zhu, Feifan Huang, Fei Yu, Yantao et al.

Source: Sensors (14248220); Jan2025, Vol. 25 Issue 1, p175, 19p

Subject Terms: PARTIALLY observable Markov decision processes, DEEP reinforcement learning, LANGUAGE models, TRAJECTORY optimization, DRONE aircraft, DEEP learning

Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes

Authors: Zhang, Min Tang, Hongyao Hao, Jianye et al.

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, 02 engineering and technology, Neural and Evolutionary Computing (cs.NE), 01 natural sciences, 0105 earth and related environmental sciences, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2209.07696

Machine learning tree trimming for faster Markov reward game solutions.

Authors: İzgi, Burhaneddin Özkaya, Murat Üre, Nazım Kemal et al.

Source: Journal of Computational Science. Dec2025, Vol. 92, pN.PAG-N.PAG. 1p.

Subject Terms: Markov processes, Machine learning, Artificial neural networks, Mathematical optimization, Decision trees, Decision making, Real-time computing, Classification

Exploring Metacognitive Experiences by Simulating Internal Decisions of Information Access.

Authors: Ukov, Teodor Tsochev, Georgi

Source: Systems; Nov2025, Vol. 13 Issue 11, p982, 33p

Subject Terms: METACOGNITION, COGNITIVE computing, ASSOCIATIVE learning, COGNITIVE testing, MARKOV processes, COGNITIVE structures

Application of Optical Satellite Data in Pavement Management: Assessing Its Value in Partially Observable Stochastic Environments.

Authors: Shahri, Mahyar Kim, Sung-Hee Sonny

Source: Journal of Performance of Constructed Facilities; Aug2025, Vol. 39 Issue 4, p1-12, 12p

Subject Terms: PAVEMENT management, MACHINE learning, PARTIALLY observable Markov decision processes, PAVEMENT testing, INFRASTRUCTURE (Economics), BOOSTING algorithms, MULTISPECTRAL imaging, REMOTE sensing

Two Decades of Automated AI Planning Methods in Construction and Fabrication: a Systematic Review.

Authors: Sherkat, Shermin Wortmann, Thomas Wortmann, Andreas

Source: ACM Computing Surveys; Nov2025, Vol. 57 Issue 11, p1-21, 21p

Subject Terms: ARTIFICIAL intelligence, DISTRIBUTED artificial intelligence, SOFTWARE engineering, PARTIALLY observable Markov decision processes, COMPUTER engineering, DEEP learning, EXPERT systems

Revolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes.

Authors: Sahu, Dinesh Nidhi Chaturvedi, Rajnish et al.

Source: Scientific Reports; 1/29/2025, Vol. 15 Issue 1, p1-21, 21p

Subject Terms: PROBABILISTIC automata, EDGE computing, DISTRIBUTED computing, MARKOV processes, DECISION making

A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation.

Authors: Li, Zuopeng Ju, Hengshuai Ren, Zepeng

Source: Future Internet; Dec2023, Vol. 15 Issue 12, p395, 21p

Subject Terms: DEEP reinforcement learning, PARTIALLY observable Markov decision processes, MACHINE learning, GAMIFICATION, EDGE computing, RESOURCE allocation, REINFORCEMENT learning, GREEDY algorithms

Exact Decomposition Approaches for Markov Decision Processes: A Survey

Authors: Cherki Daoui Mohammed Abbad Mohamed Tkiouat

Source: Advances in Operations Research. 2010:1-19

Subject Terms: Artificial intelligence, Learning and Inference in Bayesian Networks, Construct (python library), Variety (cybernetics), Markov chain, 0211 other engineering and technologies, Divide and conquer algorithms, 02 engineering and technology, Space (punctuation), Autonomic Computing and Self-Adaptive Systems, Component-Based Systems, Artificial Intelligence, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Markov process, Biology, Decomposition, Ecology, Mathematical optimization, Statistics, Computer science, Programming language, Algorithm, Operating system, FOS: Biological sciences, Computer Science, Physical Sciences, Software Reliability Assessment and Prediction, Markov decision process, State space, Software, Mathematics

Recent Advances in Reinforcement Learning for Chemical Process Control.

Authors: Devarakonda, Venkata Srikar Sun, Wei Tang, Xun et al.

Source: Processes; Jun2025, Vol. 13 Issue 6, p1791, 38p

Subject Terms: CHEMICAL process control, MACHINE learning, MARKOV processes, RESEARCH & development

Optimized task assignment and predictive maintenance for industrial machines using Markov decision process.

Authors: Nasir, Ali Mekid, Samir Sawlan, Zaid et al.

Source: Operational Research; Dec2025, Vol. 25 Issue 4, p1-31, 31p

Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows.

Authors: Grunde-McLaughlin, Madeleine Lam, Michelle S. Krishna, Ranjay et al.

Source: ACM Transactions on Computer-Human Interaction (TOCHI); Jun2025, Vol. 32 Issue 3, p1-57, 57p

Subject Terms: HUMAN-computer interaction, PARTIALLY observable Markov decision processes, MACHINE learning, LANGUAGE models, ARTIFICIAL intelligence, MACHINE translating, TEXT recognition

POMDP inference and robust solution via deep reinforcement learning: an application to railway optimal maintenance.

Authors: Arcieri, Giacomo Hoelzl, Cyprien Schwery, Oliver et al.

Source: Machine Learning; Oct2024, Vol. 113 Issue 10, p7967-7995, 29p

Subject Terms: PARTIALLY observable Markov decision processes, DEEP reinforcement learning, REINFORCEMENT learning, HIDDEN Markov models, DEEP learning, MARKOV chain Monte Carlo

Dynamic Task Offloading Scheme for Edge Computing via Meta-Reinforcement Learning.

Authors: Liu, Jiajia Xie, Peng Li, Wei et al.

Source: Computers, Materials & Continua; 2025, Vol. 82 Issue 2, p2609-2635, 27p

Subject Terms: MULTI-objective optimization, DIRECTED acyclic graphs, ADAPTIVE computing systems, DISTRIBUTED computing, ENERGY consumption, DEEP learning, EDGE computing

Landmark Based Reward Shaping in Reinforcement Learning with Hidden States

Authors: Demir, Alper Cilden, Erkin Polat, Faruk

Source: AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems

Subject Terms: Computing methodologies, Machine learning, Learning paradigms, Machine learning approaches, Reinforcement learning, Partially-observable Markov decision processes results

Time: Montreal QC Canada

Relation: 85077055997; https://hdl.handle.net/11511/53047; WOS:000474345000259

Availability: https://hdl.handle.net/11511/53047

Optimizing HVAC&R System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods.

Alternate Title: تحسين كفاءة ومستويات الراحة لنظام التدفئة والتهوية وتكييف الهواء باستخدام أساليب التحكم القائمة على التعلم الآلي. (Arabic)

Authors: Dawood, Suroor M. Homod, Raad Z. Hatami, Alireza

Source: Tikrit Journal of Engineering Sciences; 2025, Vol. 32 Issue 2, p1-20, 20p

Subject Terms: ELECTRIC power, ENERGY consumption, REINFORCEMENT learning, ENERGY industries, CONSERVATION of mass

Federated reinforcement learning with constrained markov decision processes and graph neural networks for fair and grid-constrained coordination of large-scale electric vehicle charging networks.

Authors: Zhou, Lixia Huo, Dawei Chen, Jian et al.

Source: Scientific Reports; 11/12/2025, Vol. 15 Issue 1, p1-19, 19p

Subject Terms: ELECTRIC vehicles, ELECTRIC power distribution, FEDERATED learning, DATA privacy, INFRASTRUCTURE (Economics), GRAPH neural networks, MARKOV processes, RENEWABLE energy sources

Optimizing UAV Task Allocation with Enhanced Battery Efficiency Using Semi-Markov Decision Processes.

Authors: De Alba, Abraham Flores, Alejandro García Maya, Brenda et al.

Source: Journal of Intelligent & Robotic Systems; Sep2025, Vol. 111 Issue 3, p1-19, 19p