Deep reinforcement learning

Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.

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. To address this challenge, data augmentation, which involves transforming data points to maintain class labels and provide additional valuable information, has become an effective strategy. In this paper, a deep reinforcement learning-based text augmentation method for sentiment analysis was introduced, combining reinforcement learning with deep learning. The technique uses Deep Q-Network (DQN) as the reinforcement learning method to search for an efficient augmentation strategy, employing four text augmentation transformations: random deletion, synonym replacement, random swapping, and random insertion. Additionally, various deep learning networks, including CNN, Bi-LSTM, Transformer, BERT, and XLNet, were evaluated for the training phase. Experimental findings show that the proposed technique can achieve an accuracy of 65.1% with only 20% of the dataset and 69.3% with 40% of the dataset. Furthermore, with just 10% of the dataset, the method yields an F1-score of 62.1%, rising to 69.1% with 40% of the dataset, outperforming previous approaches. Evaluation on the SemEval dataset demonstrates that reinforcement learning can efficiently augment text datasets for improved sentiment analysis results.

A social network consists of individuals and the relationships between them, which often influence each other. This influence can propagate behaviors or ideas through the network, a phenomenon known as influence propagation. This concept is crucial in applications like advertising, marketing, and public health. The influence maximization (IM) problem aims to identify key individuals in a social network who, when influenced, can maximize the spread of a behavior or idea. Given the NP-hard nature of IM, non-exact algorithms, especially metaheuristics, are commonly used. However, traditional metaheuristics like the variable neighborhood search (VNS) struggle with large networks due to vast solution spaces. This paper introduces DQVNS (Deep Q-learning Variable Neighborhood Search), which integrates VNS with deep reinforcement learning (DRL) to enhance neighborhood structure determination in VNS. By using DQVNS, we aim to achieve performance similar to population-based algorithms and utilize the information created step by step during the algorithm's execution. This adaptive approach helps the VNS algorithm choose the most suitable neighborhood structure for each situation and find better solutions for the IM problem. Our method significantly outperforms existing metaheuristics and IM-specific algorithms. DQVNS achieves a 63% improvement over population-based algorithms on various datasets. The results of implementation on different real-world social networks of varying sizes demonstrate the superiority of this algorithm compared to existing metaheuristic, IM-specific algorithms, and network-specific measures.