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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Most existing deep fake detection methods are CNN-based classification baselines trained for deep fake detection.
“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 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
“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]
The loop of 'primitive action - feedback - update procedural memory - improved action' described in the bicycle riding example is similar to reinforcement learning in AI.
“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]
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- 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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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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