Deep reinforcement learning (DRL) has achieved significant results in many machine
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …

This paper reviews recent research on resource allocation in 5G cloud‐based radio access
networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the …

Data-driven modeling emerges as a promising approach to predicting building electricity
consumption and facilitating building energy management. However, the majority of the …

Portfolio managements in financial markets involve risk management strategies and
opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim …

Artificial Intelligence (AI) approaches have been increasingly used in financial markets as
technology advances. In this research paper, we conduct a Systematic Literature Review …

Vehicle control in autonomous traffic flow is often handled using the best decision-making
reinforcement learning methods. However, unexpected critical situations make the collisions …

Finding a more efficient trading strategy has always been one of the main concerns in
financial market trading. In order to create trading strategies that lead to higher profits …

Studying the representational capacity of neural networks to learn nonlinear rewards is
necessary in a complex and nonlinear environment. Over recent years, the maximum …

In the evolving landscape of portfolio management (PM), the fusion of advanced machine
learning techniques with traditional financial methodologies has opened new avenues for …

A large amount of assets characterizes high-dimensional portfolio selection problems
compared to temporal observation. In such a high-dimensional framework, the asset …