Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
A brief review of portfolio optimization techniques
A Gunjan, S Bhattacharyya - Artificial Intelligence Review, 2023 - Springer
Portfolio optimization has always been a challenging proposition in finance and
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …
[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
[HTML][HTML] ProtInteract: A deep learning framework for predicting protein–protein interactions
Proteins mainly perform their functions by interacting with other proteins. Protein–protein
interactions underpin various biological activities such as metabolic cycles, signal …
interactions underpin various biological activities such as metabolic cycles, signal …
[HTML][HTML] Dynamic portfolio optimization with inverse covariance clustering
Market conditions change continuously. However, in portfolio investment strategies, it is hard
to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by …
to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by …
Reinforcement learning on graphs: A survey
M Nie, D Chen, D Wang - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
Graph mining tasks arise from many different application domains, including social
networks, biological networks, transportation, and E-commerce, which have been receiving …
networks, biological networks, transportation, and E-commerce, which have been receiving …
[HTML][HTML] Concept drift adaptation methods under the deep learning framework: A literature review
Q Xiang, L Zi, X Cong, Y Wang - Applied Sciences, 2023 - mdpi.com
With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …
become an integral part of decision making. At the same time, deep learning is one of the …
GraphSAGE with deep reinforcement learning for financial portfolio optimization
Q Sun, X Wei, X Yang - Expert Systems with Applications, 2024 - Elsevier
Portfolio optimization is an active management strategy that aims to maximize returns and
control risk within reasonable limits. The Proximal Policy Optimization (PPO), a robust on …
control risk within reasonable limits. The Proximal Policy Optimization (PPO), a robust on …
A mean-VaR based deep reinforcement learning framework for practical algorithmic trading
B Jin - IEEE Access, 2023 - ieeexplore.ieee.org
It is difficult to automatically produce trading signals based on previous transaction data and
the financial status of assets because of the significant noise and unpredictability of capital …
the financial status of assets because of the significant noise and unpredictability of capital …
GPM: A graph convolutional network based reinforcement learning framework for portfolio management
S Shi, J Li, G Li, P Pan, Q Chen, Q Sun - Neurocomputing, 2022 - Elsevier
Portfolio management is a decision-making process of periodically reallocating a certain
amount of funds into a portfolio of assets, with the objective of maximizing the profits …
amount of funds into a portfolio of assets, with the objective of maximizing the profits …