OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making
Abstract In Multi-Criteria Decision Making (MCDM), alternatives are evaluated by
considering different criteria. In MCDM, there is a requirement to integrate the objective and …
considering different criteria. In MCDM, there is a requirement to integrate the objective and …
Lexicographic actor-critic deep reinforcement learning for urban autonomous driving
Urban autonomous driving is a difficult task because of its complex road scenarios and the
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
[HTML][HTML] Deep reinforcement learning with the random neural network
W Serrano - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
This paper proposes a Deep Reinforcement Learning (DRL) algorithm that expands the
Random Neural Network (RNN) Reinforcement Learning (RL) method to include the …
Random Neural Network (RNN) Reinforcement Learning (RL) method to include the …
[HTML][HTML] Stock market prediction using deep reinforcement learning
AL Awad, SM Elkaffas, MW Fakhr - Applied System Innovation, 2023 - mdpi.com
Stock value prediction and trading, a captivating and complex research domain, continues to
draw heightened attention. Ensuring profitable returns in stock market investments demands …
draw heightened attention. Ensuring profitable returns in stock market investments demands …
Reinforcement learning-based denoising model for seismic random noise attenuation
C Liang, H Lin, H Ma - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
The random noise attenuation is an essential step in seismic data processing. Due to
complex geological conditions and acquisition environment, the intensity of the effective …
complex geological conditions and acquisition environment, the intensity of the effective …
A multiagent deep deterministic policy gradient-based distributed protection method for distribution network
P Zeng, S Cui, C Song, Z Wang, G Li - Neural Computing and Applications, 2023 - Springer
Relay protection system plays an important role in the safe and stable operation of
distribution network (DN), and the traditional model-based relay protection algorithms are …
distribution network (DN), and the traditional model-based relay protection algorithms are …
[PDF][PDF] Addressing challenges in dynamic modeling of stewart platform using reinforcement learning-based control approach
In this paper, we focus on enhancing the performance of the controller utilized in the Stewart
platform by investigating the dynamics of the platform. Dynamic modeling is crucial for …
platform by investigating the dynamics of the platform. Dynamic modeling is crucial for …
Multi-Agent Reinforcement Learning based Uplink OFDMA for IEEE 802.11 ax Networks
In the IEEE 802.11 ax Wireless Local Area Networks (WLANs), Orthogonal Frequency
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
[HTML][HTML] An improved multi-objective deep reinforcement learning algorithm based on envelope update
Multi-objective reinforcement learning (MORL) aims to uniformly approximate the Pareto
frontier in multi-objective decision-making problems, which suffers from insufficient …
frontier in multi-objective decision-making problems, which suffers from insufficient …
[HTML][HTML] Residential demand response strategy based on deep deterministic policy gradient
C Deng, K Wu - Processes, 2021 - mdpi.com
With the continuous improvement of the power system and the deepening of electricity
market reform, the trend of users' active participation in power distribution is more and more …
market reform, the trend of users' active participation in power distribution is more and more …