The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Transfer in Reinforcement Learning: A Framework and a Survey
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1023/a:1022676722315 · Cited by 3,907
Prediction, Learning, and Games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,162
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Schema induction and analogical transfer
Gick, M. L., & Holyoak, K. J. (1983) · Cognitive Psychology · doi:10.1016/0010-0285(83)90002-6 · Cited by 3,010 · cited 1×
“On the other hand, taking inspiration from studies in psychology and neuroscience (Gentner et al, 2003; Gick and Holyoak, 1983), the transfer point of view, where learning tasks are assumed to be related and knowledge is retained and transferred, is considered as the most suitable perspective to design effective techn…”
BackgroundConvex multi-task feature learning
Argyriou, A., Evgeniou, T., & Pontil, M. (2008) · Machine Learning · doi:10.1007/s10994-007-5040-8 · Cited by 1,528
Transfer Learning for Reinforcement Learning Domains: A Survey
Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,488
Cited by
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
A review of cooperative multi-agent deep reinforcement learning
Oroojlooy, A., & Hajinezhad, D. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04105-y · Cited by 482
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Silva, F. L. D., & Costa, A. H. R. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11396 · Cited by 273
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Eysenbach, B., & Levine, S. (2021) · arXiv · doi:10.48550/arxiv.2103.06257 · Cited by 69
Metadata
- Authors
- Lazaric, A.
- Venue
- Adaptation, Learning, and Optimization
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 143-173
- ISSN
- 1867-4534 (ISSN-L)
- 1867-4542
- OpenAlex
- W158722652
- Cited by
- 256
- References
- 66
- FWCI
- 10.93
- Type
- book chapter
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Transfer in Reinforcement Learning: A Framework and a Survey. https://app.cochat.ai/papers/10.1007/978-3-642-27645-3_5
- The paper
- Lazaric, A. (2012). Transfer in Reinforcement Learning: A Framework and a Survey. Adaptation, Learning, and Optimization. https://doi.org/10.1007/978-3-642-27645-3_5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
