Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...
The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fl...
Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem f...
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological...
Neural networks : the official journal of the International Neural Network Society
Jan 8, 2025
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, const...
Neural networks : the official journal of the International Neural Network Society
Jan 1, 2025
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is ...
Neural networks : the official journal of the International Neural Network Society
Dec 31, 2024
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhanci...
Neural networks : the official journal of the International Neural Network Society
Dec 27, 2024
Certifying robustness against external uncertainties throughout the control process to reduce the risk of instability is very important. Most existing approaches based on adversarial learning use a fixed parameter to adjust the intensity of adversari...
Neural networks : the official journal of the International Neural Network Society
Dec 19, 2024
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...
Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neura...
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