High-Performance Open-Source Archive
The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <doi:10.48550/arXiv.1805.11793>.
| Version: | 1.0 |
| Published: | 2018-05-31 |
| DOI: | 10.32614/CRAN.package.CBT |
| Author: | Hock Peng Chan and Shouri Hu |
| Maintainer: | Shouri Hu <e0054325 at u.nus.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | CBT results |
| Reference manual: | CBT.html , CBT.pdf |
| Package source: | CBT_1.0.tar.gz |
| Windows binaries: | r-devel: CBT_1.0.zip, r-release: CBT_1.0.zip, r-oldrel: CBT_1.0.zip |
| macOS binaries: | r-release (arm64): CBT_1.0.tgz, r-oldrel (arm64): CBT_1.0.tgz, r-release (x86_64): CBT_1.0.tgz, r-oldrel (x86_64): CBT_1.0.tgz |
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