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Survey on Knowledge Transfer Method in Deep Reinforcement Learning [PDF]

Jisuanji kexue, 2023
Deep reinforcement learning is a hot issue in artificial intelligence research.With the deepening of research,some shortcomings are gradually exposed,such as low data utilization,weak generalization ability,difficult exploration,lack of reasoning and ...
ZHANG Qiyang, CHEN Xiliang, CAO Lei, LAI Jun, SHENG Lei
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On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey

IEEE Access, 2021
This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata, Liliana Lo Presti, Marco La Cascia   +2 more
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Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

IEEE Access, 2023
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to ...
Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, Alexander Wong, Doina Precup   +4 more
doaj   +1 more source

An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges

Applied Sciences, 2023
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists.
Santosh Kumar Sahu, Anil Mokhade, Neeraj Dhanraj Bokde   +2 more
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Characterizing Mobile Money Phishing Using Reinforcement Learning

IEEE Access, 2023
Mobile money helps people accumulate, send, and receive money using their mobile phones without having a bank account (i.e., in some African countries). Such technology is heavily and efficiently used in many areas where bank services are unavailable and/
Alima Nzeket Njoya, Vanelle Laure Tchaya Ngongag, Franklin Tchakounte, Marcellin Atemkeng, Claude Fachkha   +4 more
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Feasibility Analysis and Application of Reinforcement Learning Algorithm Based on Dynamic Parameter Adjustment

Algorithms, 2020
Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still ...
Menglin Li, Xueqiang Gu, Chengyi Zeng, Yuan Feng   +3 more
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Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

World Electric Vehicle Journal, 2023
Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed.
Jianguo Xi, Jingwei Ma, Tianyou Wang, Jianping Gao   +3 more
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Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Mendel, 2021
Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots.
Roman Parak, Radomil Matousek
doaj   +1 more source

Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

Robotics, 2021
Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence ...
Rongrong Liu, Florent Nageotte, Philippe Zanne, Michel de Mathelin, Birgitta Dresp-Langley   +4 more
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GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents

Tongxin xuebao, 2023
To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN, Yikang LI, Zhiqiang YAO, Yulin CHEN, Jinbo XIONG   +4 more
doaj   +2 more sources