Design and Development of Multi-Agent Reinforcement Learning Intelligence on the Robotarium Platform for Embedded System Applications DOI Open Access
Lorenzo Canese, G.C. Cardarilli, Mohammad Mahdi Dehghan Pir

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(10), P. 1819 - 1819

Published: May 8, 2024

This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm robotic applications. study investigates efficacy Q-RTS in reducing convergence time to a satisfactory movement policy through successful implementation four and eight trained agents. has been shown significantly reduce search terms training iterations, from almost million iterations with one agent 650,000 agents 500,000 The scalability was addressed by testing it on several agents’ configurations. A central focus placed design sophisticated reward function, considering various postures their critical role optimizing Q-learning algorithm. Additionally, this delved into robustness agents, revealing ability adapt dynamic environmental changes. findings have broad implications improving efficiency adaptability systems applications such as IoT embedded systems. tested implemented using Georgia Tech Robotarium platform, showing its feasibility above-mentioned

Language: Английский

Reinforcement learning algorithms: A brief survey DOI
Ashish Kumar Shakya, G. N. Pillai, Sohom Chakrabarty

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 231, P. 120495 - 120495

Published: May 23, 2023

Language: Английский

Citations

182
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems DOI Creative Commons
Rafael Figueiredo Prudencio, Marcos R. O. A. Máximo, Esther Luna Colombini

et al.

IEEE Transactions on Neural Networks and Learning Systems, Journal Year: 2023, Volume and Issue: 35(8), P. 10237 - 10257

Published: March 22, 2023

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still wide range domains inaccessible RL due high cost danger interacting environment. Offline paradigm that learns exclusively static datasets collected interactions, making it feasible extract policies large diverse training datasets. Effective offline algorithms have much wider applications than online RL, being particularly appealing for real-world applications, education, healthcare, robotics. In this work, we contribute unifying taxonomy classify methods. Furthermore, provide comprehensive review latest algorithmic breakthroughs field using unified notation well existing benchmarks' properties shortcomings. Additionally, figure summarizes performance each method class methods on different dataset properties, equipping researchers tools decide which type algorithm best suited problem at hand identify classes look most promising. Finally, our perspective open problems propose future research directions rapidly growing field.

Language: Английский

Citations

137
Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations DOI
Pouyan Esmaeilzadeh

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 151, P. 102861 - 102861

Published: March 30, 2024

Language: Английский

Citations

78
A Systematic Study on Reinforcement Learning Based Applications DOI Creative Commons

Keerthana Sivamayilvelan,

R Elakkiya,

Belqasem Aljafari

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(3), P. 1512 - 1512

Published: Feb. 3, 2023

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet things security, recommendation systems, finance, and energy management. The optimization use is critical today’s environment. mainly focus on the RL application Traditional rule-based systems a set predefined rules. As result, they may become rigid unable to adjust changing situations or unforeseen events. can overcome these drawbacks. learns by exploring environment randomly based experience, it continues expand its knowledge. Many researchers are working RL-based management (EMS). utilized such as optimizing smart buildings, hybrid automobiles, grids, managing renewable resources. contributes achieving net zero carbon emissions sustainable In context technology, be optimize regulation building heating, ventilation, air conditioning (HVAC) reduce consumption while maintaining comfortable atmosphere. EMS accomplished teaching an agent make judgments sensor data, temperature occupancy, modify HVAC system settings. has proven beneficial lowering usage buildings active research area buildings. used electric vehicles (HEVs) learning optimal control policy maximize battery life fuel efficiency. acquired remarkable position gaming applications. majority security-related operate simulated recommender provide good suggestions accuracy diversity. This article assists novice comprehending foundations reinforcement

Language: Английский

Citations

68
The Benefits and Limitations of ChatGPT in Business Education and Research: A Focus on Management Science, Operations Management and Data Analytics DOI
Ivor Cribben, Yasser Zeinali

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

ChatGPT is an artificial-intelligence chatbot developed by OpenAI. It can be used in a variety of applications including content creation, personalized recommendations, copy and for language translation. In Business, it data analysis, provide even process orders. Its benefits have been discussed widely popular media with several articles focusing on the changes will bring to workforce way we live work broadly. this article, discuss limitations Business education research particular focus areas management science, operations analytics. We consider its use both professors students. For professors, design courses, create syllabi content, help grading, student understanding. students, explain complex concepts, debug code, sample exam questions. Overall, find that writing debugging code greatest strength educational purposes. However, has often makes mistakes requires deeper or advanced knowledge domain. Finally, discussion also raises problems regarding bias plagiarism.

Language: Английский

Citations

43
Deep deterministic policy gradient algorithm: A systematic review DOI Creative Commons
Ebrahim Hamid Sumiea, Said Jadid Abdulkadir, Hitham Alhussian

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30697 - e30697

Published: May 1, 2024

Language: Английский

Citations

25
Combining Lyapunov Optimization With Actor–Critic Networks for Privacy-Aware IIoT Computation Offloading DOI
Guowen Wu, Xihang Chen, Yizhou Shen

et al.

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(10), P. 17437 - 17452

Published: Jan. 22, 2024

Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet Things (IIoT) devices. However, as more and tasks are being offloaded mobile-edge (MEC) servers for processing, it can lead IIoT privacy security issues, such personal usage habits. In this paper, we aim design a Lyapunov-based privacy-aware framework that defines amount user designs "reduced privacy" mechanism. We first define cumulative each trigger protection mechanism when exceeds set threshold. The data generated by then transferred local finally, reduced. This model ensures all users remains stable. further combine advantages Lyapunov optimization actor-critic networks address problem how make learn optimal policy maintain minimum energy consumption in long run. Especially, integrates model-based model-free handle with very low computational complexity, minimizes while stabilizing queue. It demonstrated through experimental simulation results proposed scheme queue stability minimize under strict security.

Language: Английский

Citations

23
Defining intelligence: Bridging the gap between human and artificial perspectives DOI Creative Commons
Gilles E. Gignac,

Eva T. Szodorai

Intelligence, Journal Year: 2024, Volume and Issue: 104, P. 101832 - 101832

Published: April 8, 2024

Achieving a widely accepted definition of human intelligence has been challenging, situation mirrored by the diverse definitions artificial in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes refined nomenclature that harmonizes conceptualizations across two disciplines. Abstract operational for are proposed emphasize maximal capacity completing novel goals successfully through respective perceptual-cognitive computational processes. Additionally, support considering intelligence, artificial, as consistent with multidimensional model capabilities is provided. The implications current practices training testing also described, they can be expected to lead achievement or expertise rather than intelligence. Paralleling psychometrics, 'AI metrics' suggested needed science discipline acknowledges importance test reliability validity, well standardized measurement procedures system evaluations. Drawing parallels general (AGI) described reflection shared variance performances. We conclude evidence more greatly supports observation over However, interdisciplinary collaborations, based on common understandings nature sound practices, could facilitate scientific innovations help bridge gap between human-like

Language: Английский

Citations

23
LLM-Controller: Dynamic Robot Control Adaptation Using Large Language Models DOI
Rasoul Zahedifar, Mahdieh Soleymani Baghshah, Alireza Taheri

et al.

Robotics and Autonomous Systems, Journal Year: 2025, Volume and Issue: unknown, P. 104913 - 104913

Published: Jan. 1, 2025

Language: Английский

Citations

2
Exploring reinforcement learning in process control: a comprehensive survey DOI
N. Rajasekhar, T. K. Radhakrishnan, Samsudeen Naina Mohamed

et al.

International Journal of Systems Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 30

Published: March 2, 2025

Reinforcement Learning (RL) is a machine learning methodology that develops the capability to make sequential decisions in intricate issues using trial-and-error techniques. RL has become increasingly prevalent for decision-making and control tasks diverse fields such as industrial processes, biochemical systems energy management. This review paper presents comprehensive examination of development, models, algorithms practical uses RL, with specific emphasis on its application process control. The study examines fundamental theories, applications classifying them into two categories: classical Markov decision processes (MDP) deep viz., actor critic methods. topic discussion multiple industries, chemical control, systems, wastewater treatment oil gas sector. Nevertheless, also highlights challenges hinder larger acceptance, including requirement substantial computational resources, complexity simulating real-world settings challenge guaranteeing stability resilience dynamic unpredictable environments. demonstrated significant promise, but more research needed fully integrate it environmental order solve current challenges.

Language: Английский

Citations

2