Package: CBT 1.0
Shouri Hu
CBT: Confidence Bound Target Algorithm
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) <arxiv:1805.11793>.
Authors:
CBT_1.0.tar.gz
CBT_1.0.tar.gz(r-4.5-noble)CBT_1.0.tar.gz(r-4.4-noble)
CBT_1.0.tgz(r-4.4-emscripten)CBT_1.0.tgz(r-4.3-emscripten)
CBT.pdf |CBT.html✨
CBT/json (API)
# Install 'CBT' in R: |
install.packages('CBT', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:bfbed63c53. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-linux | OK | Oct 31 2024 |
Exports:Ana_CBTCBTCosine_PriorEmp_CBTSine_PriorUniform_Prior
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Confidence Bound Target (CBT) Algorithm | Ana_CBT CBT Cosine_Prior Emp_CBT Sine_Prior Uniform_Prior |