The DOI matches its Crossref record; 149 of the 162 references it cites were found in a registry or an index.
Reinforcement Learning: A Survey
Open accessAbstract
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
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In the citing paper
“Most of the existing methods are CNN-based classification baselines trained for deep fake detection [6, 11, 22, 25].”
From On Improving Cross-dataset Generalization of Deepfake Detectors (2022) · cites this paper as [11]
In the citing paper
“In reinforcement learning, positive feedback from a certain action results in a positive reward. Based on this positive reward, the parameters of the policy model are adjusted to increase the probability of selecting this action in the corresponding state. Conversely, if it is negative feedback, the parameters of the model will be adjusted to decrease the probability of selecting this action. The”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [19]
In the citing paper
“The loop of 'primitive action - feedback - update procedural memory - improved action' described in the bicycle riding example is similar to reinforcement learning in AI [19, 21, 22].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [19]
Citations
Cites
Genetic algorithms in search, optimization, and machine learning
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Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
(1995) · Cited by 10,558
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,893
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,674
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
“y Recently, many of the grand AI challenges involving game play have been overcome using DNNs. These successes also required innovations in training techniques and many rely on reinforcement learning [30]. DNNs have surpassed human level accuracy in playing Atari [31] as well as Go [6], where an exhaustive search of a…”
Background
Metadata
- Authors
- Kaelbling, L. P., Littman, M. L., & Moore, A. W.
- Affiliations
- Brown University (US)
- Carnegie Mellon University (US)
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1996-05-01
- Volume
- 4
- Pages
- 237-285
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2107726111
- Cited by
- 8,972
- References
- 162
- Citations by year
- 2026: 367 · 2025: 703 · 2024: 626 · 2023: 597 · 2022: 498 · 2021: 549 · 2020: 583 · 2019: 416 · 2018: 351 · 2017: 266 · 2016: 201 · 2015: 218 · 2014: 233 · 2013: 272 · 2012: 225
- FWCI
- 94.14
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Artificial Intelligence Research (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (9453383, 9312395)
- 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 › Machine Learning and Algorithms
- Keywords
- reinforcement learning, Markov decision theory, exploration-exploitation balance, delayed reinforcement, empirical model, generalization, hierarchical learning, hidden states, learning from interaction, agent-environment interaction
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