Results 21 to 30 of about 301,921 (265)
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 with Interactive Feedback in a Human–Robot Environment
Applied Sciences, 2020 Robots are extending their presence in domestic environments every day, it being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to ...
Ithan Moreira +5 more
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Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [PDF]
Jisuanji kexue, 2022 Multi-agent deep reinforcement learning based on value factorization is one of many multi-agent deep reinforcement learning algorithms,and it is also a research hotspot in the field of multi-agent deep reinforcement learning.Under some constraints,the ...
XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang
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Towards Continual Reinforcement Learning through Evolutionary Meta-Learning [PDF]
, 2019 In continual learning, an agent is exposed to a changing environment, requiring it to adapt during execution time. While traditional reinforcement learning (RL) methods have shown impressive results in various domains, there has been less progress in ...
Grbic, Djordje, Risi, Sebastian
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Real-time security margin control using deep reinforcement learning
Energy and AI, 2023 This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit.
Hannes Hagmar +2 more
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Abstraction for Deep Reinforcement Learning
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability.
Shanahan, M, Mitchell, M
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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Sensors, 2023 Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent
James Orr, Ayan Dutta
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Restoring chaos using deep reinforcement learning [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020 A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has been quite challenging. We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently chaotic regime of the ...
Sumit Vashishtha, Siddhartha Verma
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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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Learning an Efficient Text Augmentation Strategy: A Case Study in Sentiment Analysis [PDF]
International Journal of Web Research, 2023 Contemporary machine learning models, like deep neural networks, require substantial labeled datasets for proper training. However, in areas such as natural language processing, a shortage of labeled data can lead to overfitting.
Mehdy Roayaei
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