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.
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“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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A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 799
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 340
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
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