[HTML][HTML] A systematic study on reinforcement learning based applications
K Sivamayil, E Rajasekar, B Aljafari, S Nikolovski… - Energies, 2023 - mdpi.com
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Policy mirror ascent for efficient and independent learning in mean field games
Mean-field games have been used as a theoretical tool to obtain an approximate Nash
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …
learning to learn profitable trading strategies from financial market data. However, a single …
SCA-MADRL: Multiagent deep reinforcement learning framework based on state classification and assignment for intelligent shield attitude control
J Xu, J Bu, N Qin, D Huang - Expert Systems with Applications, 2024 - Elsevier
With the wide application of the shield tunneling method in tunnel engineering, the untimely
and incorrect attitude control of shield systems has become an essential factor affecting the …
and incorrect attitude control of shield systems has become an essential factor affecting the …
Research on carbon asset trading strategy based on PSO-VMD and deep reinforcement learning
J Zhang, K Chen - Journal of Cleaner Production, 2024 - Elsevier
As the financialization of carbon emission right, developing effective carbon asset trading
strategy is important for both investors and regulators. Traditional trading strategy based on …
strategy is important for both investors and regulators. Traditional trading strategy based on …
Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction
DO Oyewola, SA Akinwunmi… - Knowledge-Based Systems, 2024 - Elsevier
Accurate prediction of stock market trends and movements holds great significance in the
financial industry as it enables investors, traders, and decision-makers to make informed …
financial industry as it enables investors, traders, and decision-makers to make informed …
Distributed dynamic pricing of multiple perishable products using multi-agent reinforcement learning
Revenue management (RM) is essential for a wide range of industries such as airlines,
hotels, cruise lines, fashion, and seasonal retail. This paper focuses on the multi-perishable …
hotels, cruise lines, fashion, and seasonal retail. This paper focuses on the multi-perishable …
Practical robust fixed-time containment control for multi-agent systems under actuator faults
Z Zhu, Y Yin, F Wang, Z Liu, Z Chen - Expert Systems with Applications, 2024 - Elsevier
This article considers a containment control problem for nonlinear multi-agent systems with
nonautonomous leaders under directed topology, where each follower may experience …
nonautonomous leaders under directed topology, where each follower may experience …
A multi-agent virtual market model for generalization in reinforcement learning based trading strategies
Many studies have successfully used reinforcement learning (RL) to train an intelligent
agent that learns profitable trading strategies from financial market data. Most of RL trading …
agent that learns profitable trading strategies from financial market data. Most of RL trading …