The applicability of reinforcement learning methods in the development of industry 4.0 applications
Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …
problems. Our article gives a systematic overview of major types of RL methods, their …
A review of deep reinforcement learning approaches for smart manufacturing in industry 4.0 and 5.0 framework
A del Real Torres, DS Andreiana, Á Ojeda Roldán… - Applied Sciences, 2022 - mdpi.com
In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …
Deep reinforcement learning in production planning and control: a systematic literature review
Increasingly fast development cycles and individualized products pose major challenges for
today's smart production systems in times of industry 4.0. The systems must be flexible and …
today's smart production systems in times of industry 4.0. The systems must be flexible and …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
Machine learning in manufacturing towards industry 4.0: From 'for now'to 'four-know'
T Chen, V Sampath, MC May, S Shan, OJ Jorg… - Applied Sciences, 2023 - mdpi.com
While attracting increasing research attention in science and technology, Machine Learning
(ML) is playing a critical role in the digitalization of manufacturing operations towards …
(ML) is playing a critical role in the digitalization of manufacturing operations towards …
A review on reinforcement learning algorithms and applications in supply chain management
Decision-making in supply chains is challenged by high complexity, a combination of
continuous and discrete processes, integrated and interdependent operations, dynamics …
continuous and discrete processes, integrated and interdependent operations, dynamics …
[PDF][PDF] A comprehensive evaluation and comparison of enhanced learning methods
This paper provides a comprehensive evaluation and comparison of current reinforcement
learning methods. By analyzing the strengths and weaknesses of the main methods, such as …
learning methods. By analyzing the strengths and weaknesses of the main methods, such as …
A bibliometric analysis and review on reinforcement learning for transportation applications
Transportation is the backbone of the economy and urban development. Improving the
efficiency, sustainability, resilience, and intelligence of transportation systems is critical and …
efficiency, sustainability, resilience, and intelligence of transportation systems is critical and …
Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
A review of the applications of multi-agent reinforcement learning in smart factories
F Bahrpeyma, D Reichelt - Frontiers in Robotics and AI, 2022 - frontiersin.org
The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing
advanced manufacturing systems and realizing modern manufacturing objectives such as …
advanced manufacturing systems and realizing modern manufacturing objectives such as …