Results 11 to 20 of about 1,052,376 (334)

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

Job-Scheduling-Deep-Reinforcement-Learning

, 2023
A novel approach to Job Scheduling applying Deep Reinforcement Learning and Neural Network Architectures of Natural Language ProcessingIf you use this software, please cite it as ...
de Oliveira Hitzges, Diego
core   +2 more sources

A survey on deep reinforcement learning for audio‑based applications [PDF]

, 2023
Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world.
Ali, Hafiz Shehbaz, Latif, Siddique, Shamshad, Fahad, Pervez, Farrukh, Cambria, Erik, Cuayahuitl, Heriberto   +5 more
core   +1 more source

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

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

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