The DOI matches its Crossref record.
An Introduction to Deep Reinforcement Learning
Abstract
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, deep reinforcement learning models (30, 31) are based on well-known concepts such as the Markov decision process (MDP) (32) and Q-learning (33) adapted to neural networks.”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [31]
Citations
Cited by
Sorted by citations
Items 501–515 of 515
Cryptocurrency portfolio management using YOLO compared with deep reinforcement learning
Hussain, T., & Ramamoorthy, M. (2025) · AIP conference proceedings · doi:10.1063/5.0277297
Optimizing Intrusion Detection Systems with Q-Learning Techniques
Thanh, C. T., & Hung, D. P. (2025) · Signals and communication technology · doi:10.1007/978-3-032-03632-2_9
Evolutionary Reinforcement Learning With Late-Start Evolution and Clustering Archive
Cai, Q., Jia, Y.-H., Zheng, K., Ou, S. S., & Chen, W. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3627631
Unveiling the Impact: Societal Implications of Reinforcement Learning Algorithms
Khanna, H., Mehra, M., & Diogo, J. F. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815322316125010008
Xu, F., Wang, Y., Gulliver, T. A., Xie, Y., Wang, C., Jiang, R., Bao, T., Lim, E. G., Lin, Z., & Samy, R. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3611642
REPA: Resilient and Proactive Resource Allocation for Last-Mile Heterogeneous Edge-IoT Systems
AlQerm, I., Cheng, N., & Pan, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3629135
A Study on the Impact of Artificial Intelligence on the Efficiency of the Manufacturing System
Mohammed, A., Koleva, N., & Nikolov, B. (2025) · doi:10.1109/telecom66943.2025.11304066
Unsupervised pretraining of state representations in a rewardless environment
Merckling, A. (2021) · theses.fr (ABES) · doi:10.70675/e5589029zb8c4z4364zb923zbb4c70deaaf9
Barbieri, E. (2023) · theses.fr (ABES) · doi:10.70675/94042ff8zd2bcz4e4cza593z12abe2190d76 · Cited by 2
Deep Reinforcement Learning for Optimal Energy Management in Smart Multi-Energy Systems
Bousnina, D. (2023) · theses.fr (ABES) · doi:10.70675/3a3c5c6czb323z43e2zb6a1z89b9b18be1b5 · Cited by 2
Reduced-Order Modeling of CO2 Storage in Complex 3D Aquifers Using the E2CO Framework
Yehia, T., Falola, Y., Eloghosa, I., Nair, G. G., Toms, J., & Meehan, D. N. (2026) · SPE Western Regional Meeting · doi:10.2118/232859-ms
27Chapter 2 Mathematical Foundations in Reinforcement Learning
Velpula, S., Ashok, P., Lakshmi Sridevi, S., & Anandaram, H. (2026) · De Gruyter eBooks · doi:10.1515/9783111633909-002
BookBuddy: A Reinforcement Learning Based Book Recommender System
Bansal, M., Shivanka, Kaur, G., -, I. G., Aggarwal, V., Pathak, N., & Jain, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-21901-5_10
Artificial Social Intelligence
Tennenholtz, M., & Madmon, O. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818663
Metadata
- Authors
- François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J.
- Affiliations
- McGill University (CA)
- Google (United States) (US)
- Venue
- now publishers, Inc. eBooks
- Publisher
- now Publishers Inc
- Published
- 2018
- OpenAlex
- W3123212791
- Cited by
- 544
- References
- 0
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 25 · 2023: 83 · 2022: 101 · 2021: 148 · 2020: 116 · 2019: 44 · 2018: 1
- FWCI
- 26.25
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- now publishers, Inc. eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- deep reinforcement learning, reinforcement learning, deep learning, decision-making, generalization, practical application, robotics, health care, smart grid, finance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
