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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.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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These binaries (installable software) and packages are in development.
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