The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Gambling in a rigged casino: The adversarial multi-armed bandit problem
Open accessAbstract
In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected per-round payoff of our algorithm approaches that of the best arm at the rate O(T/sup -1/3/), and we give an improved rate of convergence when the best arm has fairly low payoff. We also consider a setting in which the player has a team of "experts" advising him on which arm to play; here, we give a strategy that will guarantee expected payoff close to that of the best expert. Finally, we apply our result to the problem of learning to play an unknown repeated matrix game against an all-powerful adversary.
Overview
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
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Self-citationAsymptotically efficient adaptive allocation rules
Lai, T. L., & Robbins, H. (1985) · Advances in Applied Mathematics · doi:10.1016/0196-8858(85)90002-8 · Cited by 2,406
Some aspects of the sequential design of experiments
Robbins, H. (1952) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1952-09620-8 · Cited by 2,184
The Weighted Majority Algorithm
Littlestone, N., & Warmuth, M. K. (1994) · Information and Computation · doi:10.1006/inco.1994.1009 · Cited by 1,975
Multi-Armed Bandit Allocation Indices.
Bather, J. A., & Gittins, J. C. (1990) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2982809 · Cited by 998
Discrete Parameter Martingales
(2006) · Springer eBooks · doi:10.1007/978-0-387-35434-7_14 · Cited by 682
Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D. P., Schapire, R. E., & Warmuth, M. K. (1997) · Journal of the ACM · doi:10.1145/258128.258179 · Cited by 634
Self-citationVovk, V. G. (1990) · Conference on Learning Theory · doi:10.5555/92571.92672 · Cited by 551
Adaptive Game Playing Using Multiplicative Weights
Freund, Y., & Schapire, R. E. (1999) · Games and Economic Behavior · doi:10.1006/game.1999.0738 · Cited by 515
Self-citation
Cited by
A Survey of Monte Carlo Tree Search Methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2012.2186810 · Cited by 2,970
Online learning: A comprehensive survey
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.112 · Cited by 746
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
Naparstek, O., & Cohen, K. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2879433 · Cited by 477
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
Even-Dar, E., Mannor, S., & Mansour, Y. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45435-7_18 · Cited by 298
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T. (2017) · arXiv · doi:10.48550/arxiv.1711.00832 · Cited by 295
Metadata
- Authors
- Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. E.
- Venue
- Proceedings of IEEE 36th Annual Foundations of Computer Science
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Volume
- 7
- Pages
- 322-331
- ISSN
- 0272-5428 (ISSN-L)
- OpenAlex
- W305949787
- Cited by
- 742
- References
- 17
- FWCI
- 23.54
- Type
- conference paper
- 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: Gambling in a rigged casino: The adversarial multi-armed bandit problem. https://app.cochat.ai/papers/10.1109/sfcs.1995.492488
- The paper
- Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. E. (2002). Gambling in a rigged casino: The adversarial multi-armed bandit problem. Proceedings of IEEE 36th Annual Foundations of Computer Science. https://doi.org/10.1109/sfcs.1995.492488
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.
