Results 11 to 20 of about 50 (48)
Deep Reinforcement Learning for Multiobjective Optimization [PDF]
IEEE Transactions on Cybernetics, 2021 This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems. Then each subproblem is modelled as a neural network.
Kaiwen Li, Tao Zhang, Rui Wang
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Deep Ordinal Reinforcement Learning [PDF]
, 2020 replaced figures for better visibility, added github repository, more details about source of experimental results, updated target value calculation for standard and ordinal Deep Q ...
Johannes Fürnkranz +2 more
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Scenario-assisted Deep Reinforcement Learning [PDF]
Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, 2022 Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (
Yerushalmi, Raz +5 more
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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.
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Deep Reinforcement Learning: A Brief Survey [PDF]
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from ...
Kai Arulkumaran +3 more
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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 +3 more
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Deep Reinforcement Learning: An Overview [PDF]
, 2017 Proceedings of SAI Intelligent Systems Conference (IntelliSys ...
Michael Schukat +2 more
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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 +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 +3 more
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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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