Results 41 to 50 of about 5,156,964 (309)
CoRR, 2020 To appear in proceedings of the 37th International Conference on Machine ...
Ibrahim El Shar, Daniel R. Jiang
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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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Uncertainty-aware Path Planning using Reinforcement Learning and Deep Learning Methods [PDF]
Computer and Knowledge Engineering, 2020 This paper proposes new algorithms to improve Reinforcement Learning (RL) and Deep Q-Network (DQN) methods for path planning considering uncertainty in the perception of environment.
Nematollah Ab azar +2 more
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Machine Learning, 1992 Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
Watkins, C., Dayan, P.
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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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Advances in Neural Information Processing Systems 37Q-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.
Han-Dong Lim, Donghwan Lee 0002
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Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity
CoRR, 2021 An improvement of Q-learning is proposed in this paper. It is different from classic Q-learning in that the similarity between different states and actions is considered in the proposed method. During the training, a new updating mechanism is used, in which the Q value of the similar state-action pairs are updated synchronously. The proposed method can
Wei Liao, Xiaohui Wei, Jizhou Lai
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Communications BiologyFragile X syndrome (FXS) is a neurodevelopmental disorder caused by hypermethylation of expanded CGG repeats (>200) in the FMR1 gene leading to gene silencing and loss of Fragile X Messenger Ribonucleoprotein (FMRP) expression. FMRP plays important roles
James J. Fink +20 more
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Complexification through gradual involvement and reward Providing in deep reinforcement learning
Системный анализ и прикладная информатикаTraining a relatively big neural network within the framework of deep reinforcement learning that has enough capacity for complex tasks is challenging. In real life the process of task solving requires system of knowledge, where more complex skills are ...
E. V. Rulko,
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