A survey on deep reinforcement learning algorithms for robotic manipulation
Robotic manipulation challenges, such as grasping and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …
tackled successfully with the help of deep reinforcement learning systems. We give an …
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 …
[HTML][HTML] Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics
The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings
In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …
proved to be effective in optimizing the management of integrated energy systems in …
A comprehensive survey of machine learning methodologies with emphasis in water resources management
M Drogkoula, K Kokkinos, N Samaras - Applied Sciences, 2023 - mdpi.com
This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …
algorithms, highlighting their practical applications in the critical domain of water resource …
Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles
Connected automated vehicles (CAVs) are broadly recognized as next-generation
transformative transportation technologies having great potential to improve traffic safety …
transformative transportation technologies having great potential to improve traffic safety …
Towards real-time reinforcement learning control of a wave energy converter
The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil
fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to …
fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to …
Research on PID parameter tuning and optimization based on SAC-auto for USV path following
L Song, C Xu, L Hao, J Yao, R Guo - Journal of Marine Science and …, 2022 - mdpi.com
Unmanned surface vessels (USVs) are required to follow a path during a task. This is
essential for the USV, especially when following a curvilinear path or considering the …
essential for the USV, especially when following a curvilinear path or considering the …
Comparing deep reinforcement learning algorithms' ability to safely navigate challenging waters
TN Larsen, HØ Teigen, T Laache… - Frontiers in Robotics …, 2021 - frontiersin.org
Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives
of path following and collision avoidance. However, finding which RL algorithm setup …
of path following and collision avoidance. However, finding which RL algorithm setup …
[HTML][HTML] LIRL: Latent Imagination-Based Reinforcement Learning for Efficient Coverage Path Planning
Coverage Path Planning (CPP) in unknown environments presents unique challenges that
often require the system to maintain a symmetry between exploration and exploitation in …
often require the system to maintain a symmetry between exploration and exploitation in …