Results 21 to 30 of about 8,669,620 (261)
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
doaj +1 more source
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
doaj +1 more source
Steiner tree: a deep reinforcement learning approach
, 2021 Tong, GuangmoThe Steiner tree problem is a classical combinatorial optimization problem that targets interconnecting a set of points by a network whose total length is the shortest, where the network consists of the original points and the newly added ...
Wang, Siqi
core +1 more source
Deep Reinforcement Learning Approaches for the Game of Briscola [PDF]
, 2023 openReinforcement learning is increasingly becoming one of the most interesting areas of research in recent years. It is a machine learning approach that aims to design autonomous agents capable of learning from interaction with the envi- ronment ...
SINGH, AMANPREET
core
A novel policy for pre-trained deep reinforcement learning for speech emotion recognition
, 2022 Deep Reinforcement Learning (deep RL) has gained tremendous success in gaming but it has rarely been explored for Speech Emotion Recognition (SER). In the RL literature, policy used by the RL agent plays a major role in action selection, however, there ...
Rana, Rajib +10 more
core +1 more source
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
doaj +1 more source
Self-Communicating Deep Reinforcement Learning Agents Develop External Number Representations [PDF]
, 2022 Symbolic numbers are a remarkable product ofhuman cultural development. The developmentalprocess involved the creation and progressive re-finement of material representational tools, suchas notched tallies, knotted strings, and countingboards.
Petruzzellis, Flavio +7 more
core +1 more source
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
doaj +1 more source
Spectral normalisation for deep reinforcement learning: an optimisation perspective [PDF]
, 2021 Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the ...
Berariu, Tudor +5 more
core
Applied Sciences, 2018 To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed.
Linfei Yin +3 more
doaj +1 more source