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 …
A parallel deep reinforcement learning framework for controlling industrial assembly lines
Decision-making in a complex, dynamic, interconnected, and data-intensive industrial
environment can be improved with the assistance of machine-learning techniques. In this …
environment can be improved with the assistance of machine-learning techniques. In this …
Multiple Self-Supervised Auxiliary Tasks for Target-Driven Visual Navigation Using Deep Reinforcement Learning
W Zhang, L He, H Wang, L Yuan, W Xiao - Entropy, 2023 - mdpi.com
Visual navigation based on deep reinforcement learning requires a large amount of
interaction with the environment, and due to the reward sparsity, it requires a large amount …
interaction with the environment, and due to the reward sparsity, it requires a large amount …
[HTML][HTML] FGRL: Federated growing reinforcement learning for resilient mapless navigation in unfamiliar environments
Featured Application This work is motivated by practical applications, such as smart factories
and warehouses, where unmanned ground vehicles (UGVs) are required to efficiently …
and warehouses, where unmanned ground vehicles (UGVs) are required to efficiently …
Methods and Applications of Deep Reinforcement Learning for Chemical Processes
CD Hubbs - 2021 - search.proquest.com
Planning and scheduling are critical operational roles to any manufacturing business. In
most companies in the chemical industry, these roles are handled by human planners and …
most companies in the chemical industry, these roles are handled by human planners and …