Results 11 to 20 of about 298,347 (266)

Deep Residual Reinforcement Learning

, 2020
We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms ...
Boehmer, Wendelin, Whiteson, Shimon, Zhang, Shangtong   +2 more
core   +3 more sources

Deep Reinforcement Learning that Matters

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to ...
Bachman, Philip, Henderson, Peter, Islam, Riashat, Meger, David, Pineau, Joelle, Precup, Doina   +5 more
core   +2 more sources

Learning Mobile Manipulation through Deep Reinforcement Learning [PDF]

Sensors, 2020
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator.
Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, Sen Wang   +7 more
doaj   +4 more sources

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
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +1 more source

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