scholarly journals VARIOUS APPROACHES TO APPLYING REINFORCEMENT LEARNING TECHNOLOGY IN ALGORITHMIC TRADING

2021 ◽  
pp. 86-91
Author(s):  
S.T. Gataullin ◽  
I.Ya. Khasanshin ◽  
P.V. Nikitin ◽  
D.N. Semenov ◽  
V.I. Kruglov ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Algorithmic Trading ◽  
Learning Technology

Deep Reinforcement Learning for Algorithmic Trading

2021 ◽  
Author(s):  
Álvaro Cartea ◽  
Sebastian Jaimungal ◽  
Leandro Sánchez-Betancourt
Keyword(s):  
Reinforcement Learning ◽  
Algorithmic Trading

scholarly journals Phát triển Framework ứng dụng AI hỗ trợ tự động khai thác lỗ hổng bảo mật

2022 ◽  
Vol 1 (13) ◽  
pp. 80-92
Author(s):  
Nguyễn Mạnh Thiên ◽  
Phạm Đăng Khoa ◽  
Nguyễn Đức Vượng ◽  
Nguyễn Việt Hùng
Keyword(s):  
Information System ◽  
Reinforcement Learning ◽  
Information Security ◽  
Vulnerability Assessment ◽  
Large Scale ◽  
Learning Technology ◽  
Security Assessment ◽  
Security Vulnerability ◽  
Different Levels

Tóm tắt—Hiện nay, nhiệm vụ đánh giá an toàn thông tin cho các hệ thống thông tin có ý nghĩa quan trọng trong đảm bảo an toàn thông tin. Đánh giá/khai thác lỗ hổng bảo mật cần được thực hiện thường xuyên và ở nhiều cấp độ khác nhau đối với các hệ thống thông tin. Tuy nhiên, nhiệm vụ này đang gặp nhiều khó khăn trong triển khai diện rộng do thiếu hụt đội ngũ chuyên gia kiểm thử chất lượng ở các cấp độ khác nhau. Trong khuôn khổ bài báo này, chúng tôi trình bày nghiên cứu phát triển Framework có khả năng tự động trinh sát thông tin và tự động lựa chọn các mã để tiến hành khai thác mục tiêu dựa trên công nghệ học tăng cường (Reinforcement Learning). Bên cạnh đó Framework còn có khả năng cập nhật nhanh các phương pháp khai thác lỗ hổng bảo mật mới, hỗ trợ tốt cho các cán bộ phụ trách hệ thống thông tin nhưng không phải là chuyên gia bảo mật có thể tự động đánh giá hệ thống của mình, nhằm giảm thiểu nguy cơ từ các cuộc tấn công mạng. Abstract—Currently, security assessment is one of the most important proplem in information security. Vulnerability assessment/exploitation should be performed regularly with different levels of complexity for each information system. However, this task is facing many difficulties in large-scale deployment due to the lack of experienced testing experts. In this paper, we proposed a Framework that can automatically gather information and automatically select suitable module to exploit the target based on reinforcement learning technology. Furthermore, our framework has intergrated many scanning tools, exploited tools that help pentesters doing their work. It also can be easily updated new vulnerabilities exploit techniques.

scholarly journals Car Following Model and Algorithm Design based on Reinforcement Learning

2021 ◽  
Vol 2083 (3) ◽  
pp. 032008
Author(s):  
Jie Ren
Keyword(s):  
Reinforcement Learning ◽  
Algorithm Design ◽  
Unmanned Vehicles ◽  
Learning Technology ◽  
Car Following ◽  
Experimental Environment ◽  
The Future ◽  
Car Following Model

Abstract Based on reinforcement learning technology, this paper establishes a new driverless car following model. DQN algorithm and traffic simulator are mainly used to train the agent, and the following model is finally obtained. Under the precise and controllable experimental environment, the preset optimization targets can achieve the expected assumption and complete the following behavior. This study will contribute to the development of unmanned vehicles in the future.

AUTOMATED VULNERABILITY SEARCH IN A WEB APPLICATION BASED ON REINFORCEMENT LEARNING

2021 ◽  
Vol 53 (1) ◽  
pp. 91-97
Author(s):  
OLGA N. VYBORNOVA ◽  
◽  
ALEKSANDER N. RYZHIKOV ◽  
Keyword(s):  
Reinforcement Learning ◽  
Web Application ◽  
Web Applications ◽  
Subject Area ◽  
Learning Technology ◽  
Web Application Security ◽  
Vulnerability Scanner ◽  
Learning Agent ◽  
Markov Decision ◽  
Python Programming

We analyzed the urgency of the task of creating a more efficient (compared to analogues) means of automated vulnerability search based on modern technologies. We have shown the similarity of the vulnerabilities identifying process with the Markov decision-making process and justified the feasibility of using reinforcement learning technology for solving this problem. Since the analysis of the web application security is currently the highest priority and in demand, within the framework of this work, the application of the mathematical apparatus of reinforcement learning with to this subject area is considered. The mathematical model is presented, the specifics of the training and testing processes for the problem of automated vulnerability search in web applications are described. Based on an analysis of the OWASP Testing Guide, an action space and a set of environment states are identified. The characteristics of the software implementation of the proposed model are described: Q-learning is implemented in the Python programming language; a neural network was created to implement the learning policy using the tensorflow library. We demonstrated the results of the Reinforcement Learning agent on a real web application, as well as their comparison with the report of the Acunetix Vulnerability Scanner. The findings indicate that the proposed solution is promising.

Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

2012 ◽  
Vol 198-199 ◽  
pp. 922-926
Author(s):  
Run Ying Wang ◽  
Lin Xu
Keyword(s):  
Reinforcement Learning ◽  
Support Vector ◽  
Learning Technology ◽  
Risk Minimization ◽  
Machine Model ◽  
Efficient Management ◽  
Dam Management ◽  
Training Samples ◽  
Multi Agent ◽  
Use Of Knowledge

In order to achieve efficient management of the dam, the new algorithms such as reinforcement learning, Synergetic, Structural Risk Minimization and Particle Swarm Optimization are used to establish a Cooperative Wavelet Least Squares Support Vector Machine Model. To improve the convergence rate and make full use of knowledge and advice of mechanics and hydraulics of the dam, WLS-SVRM and WLS-SVCM models are used cooperatively. Before the training online, mapping provides training samples for WLS-SVCM. During the course of training online, the numerical simulation and WLS-SVCM will provide knowledge and advices for WLS-SVRM. Case study shows that the model can provide timely information of gate opening and management information of the dam so as to provide decision support for engineering management.

scholarly journals Deep Robust Reinforcement Learning for Practical Algorithmic Trading

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108014-108022 ◽  
Author(s):  
Yang Li ◽  
Wanshan Zheng ◽  
Zibin Zheng
Keyword(s):  
Reinforcement Learning ◽  
Algorithmic Trading

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

Methodology for eliminating imbalance of image data sets

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Tatyana Biloborodova ◽  
Inna Skarga-Bandurova ◽  
Mark Koverga
Keyword(s):  
Feature Extraction ◽  
Reinforcement Learning ◽  
Key Words ◽  
Class Imbalance ◽  
Image Data ◽  
Unbalanced Data ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Image Fragment

The methodology of solving the problem of eliminating class imbalance in image data sets is presented. The proposed methodology includes the stages of image fragment extraction, fragment augmentation, feature extraction, duplication of minority objects, and is based on reinforcement learning technology. The degree of imbalance indicator was used as a measure to determine the imbalance of the data set. An experiment was performed using a set of images of the faces of patients with skin rashes, annotated according to the severity of acne. The main steps of the methodology implementation are considered. The results of the classification showed the feasibility of applying the proposed methodology. The accuracy of classification on test data was 85%, which is 5% higher than the result obtained without the use of the proposed methodology. Key words: class imbalance, unbalanced data set, image fragment extraction, augmentation.

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