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.
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Deep reinforcement learning models are based on the Markov decision process and Q-learning adapted to neural networks.
“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]
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Song, D., Gan, W., Yao, P., Zang, W., Zhang, Z., & Qu, X. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.110947 · Cited by 53 · see citing works
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- 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 · 2017: 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 ›
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