Results 11 to 20 of about 1,056,132 (188)

Deep Reinforcement Learning that Matters [PDF]

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
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 ...
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, D. Meger   +5 more
semanticscholar   +3 more sources

An Introduction to Deep Reinforcement Learning [PDF]

Foundations and Trends® in Machine Learning, 2018
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau   +4 more
semanticscholar   +4 more sources

Explainability in deep reinforcement learning [PDF]

Knowledge-Based Systems, 2021
Article accepted at Knowledge-Based ...
Alexandre Heuillet, Fabien Couthouis, Natalia Díaz Rodríguez   +2 more
openaire   +3 more sources

Deep Reinforcement Learning with Adjustments [PDF]

2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 2021
Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated problems in recent years. However, their application on real-world physical systems remains limited.
Hamed Khorasgani, Haiyan Wang 0003, Chetan Gupta 0001, Susumu Serita   +3 more
openaire   +2 more sources

Deep Reinforcement Learning: An Overview [PDF]

CoRR, 2017
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant ...
Seyed Sajad Mousavi, Michael Schukat, Enda Howley   +2 more
openaire   +4 more sources

Deep-attack over the deep reinforcement learning

Knowledge-Based Systems, 2022
Accepted to Knowledge-Based ...
Yang Li 0055, Quan Pan 0001, Erik Cambria   +2 more
openaire   +3 more sources

The Primacy Bias in Deep Reinforcement Learning [PDF]

International Conference on Machine Learning, 2022
This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later.
Evgenii Nikishin, Max Schwarzer, P. D'Oro, Pierre-Luc Bacon, Aaron C. Courville   +4 more
semanticscholar   +1 more source

Rainbow: Combining Improvements in Deep Reinforcement Learning [PDF]

AAAI Conference on Artificial Intelligence, 2017
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN
Matteo Hessel, Joseph Modayil, H. V. Hasselt, T. Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, M. G. Azar, David Silver   +9 more
semanticscholar   +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