Results 41 to 50 of about 171,350 (313)
A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Genetic algorithms (GAs) belong to the category of evolutionary algorithms and are frequently utilized for resolving challenging combinatorial problems.
Christian Perez +2 more
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Frontiers in Cell and Developmental Biology, 2022 Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure
Jiacheng Sun +14 more
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, 2022 Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm that converges when linear function approximation is used.
Lim, Han-Dong, Lee, Donghwan
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Ramp Metering Control Based on the Q-Learning Algorithm
Cybernetics and Information Technologies, 2015 Modern urban highways are under the influence of increased traffic demand and cannot fulfill the desired level of service anymore. In most of the cases there is no space available for any infrastructure building.
Ivanjko Edouard +5 more
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Empirical explorations of strategic reinforcement learning: a case study in the sorting problem [PDF]
Proceedings of the Estonian Academy of Sciences, 2020 Recent advances in deep learning and reinforcement learning have made it possible to create an agent that is capable of mimicking human behaviours. In this paper, we are interested in how the reinforcement learning agent behaves under different learning ...
Ching-Sheng Lin +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias.
Zhu, Rong, Rigotti, Mattia
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Offloading decision algorithm based on reinforcement learning for mobile edge computing
Dianzi Jishu Yingyong, 2021 For the problem of computing offloading decision in mobile edge computing, this paper proposes an offloading decision algorithm based on enhanced learning in multiuser MEC system.
Yang Ge, Zhang Heng
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Safe Q-learning for continuous-time linear systems [PDF]
arXiv, 2023 Q-learning is a promising method for solving optimal control problems for uncertain systems without the explicit need for system identification. However, approaches for continuous-time Q-learning have limited provable safety guarantees, which restrict their applicability to real-time safety-critical systems.
arxiv
Lifting the Veil: Unlocking the Power of Depth in Q-learning [PDF]
arXiv, 2023 With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great success in numerous applications, including recommender systems, supply chains, games, and robotic manipulation.
arxiv
, 2022 We consider $Q$-learning with knowledge transfer, using samples from a target reinforcement learning (RL) task as well as source samples from different but related RL tasks. We propose transfer learning algorithms for both batch and online $Q$-learning with offline source studies.
Chen, Elynn Y. +2 more
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