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Deep Reinforcement Learning in Medicine [PDF]

Kidney Diseases, 2018
Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks.
openaire   +3 more sources

Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

IEEE Access, 2020
Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks.
Qilei Zhang, Jinying Lin, Qixin Sha, Bo He, Guangliang Li   +4 more
doaj   +1 more source

Learn to Steer through Deep Reinforcement Learning [PDF]

Sensors, 2018
It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-
Keyu Wu, Mahdi Abolfazli Esfahani, Shenghai Yuan, Han Wang   +3 more
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Deep Reinforcement Learning: An Overview [PDF]

, 2017
Proceedings of SAI Intelligent Systems Conference (IntelliSys ...
Michael Schukat, Enda Howley, Seyed Sajad Mousavi   +2 more
openaire   +4 more sources

Deep Reinforcement Learning for Drone Delivery [PDF]

Drones, 2019
Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known to be in the path, pilots must build a flight plan to avoid them.
Muñoz Ferran, Guillem, Barrado Muxí, Cristina, Çetin, Ender, Salamí San Juan, Esther   +3 more
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Deep Reinforcement Learning for Inventory Control: A Roadmap [PDF]

SSRN Electronic Journal, 2021
Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice.
Robert N. Boute, Joren Gijsbrechts, Willem van Jaarsveld, Nathalie Vanvuchelen   +3 more
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Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

Applied Sciences, 2018
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed.
Linfei Yin, Lulin Zhao, Tao Yu, Xiaoshun Zhang   +3 more
doaj   +1 more source

Deep-attack over the deep reinforcement learning

Knowledge-Based Systems, 2022
Accepted to Knowledge-Based ...
Yang Li, Quan Pan, Erik Cambria
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A Method to Plan the Path of a Robot Utilizing Deep Reinforcement Learning and Multi-Sensory Information Fusion

Applied Artificial Intelligence, 2023
Nowadays, mobile robots are being widely employed in various settings, including factories, homes, and everyday tasks. Achieving successful implementation of autonomous robot movement largely depends on effective route planning.
Jieren Tan
doaj   +1 more source

Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey [PDF]

arXiv, 2021
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision.
arxiv