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GPGame: Solving Complex Game Problems using Gaussian Processes
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <doi:10.48550/arXiv.1902.06565>.
| Version: |
1.2.0 |
| Imports: |
Rcpp (≥ 0.12.5), DiceKriging, GPareto, KrigInv, DiceDesign, MASS, mnormt, mvtnorm, methods, matrixStats |
| LinkingTo: |
Rcpp |
| Suggests: |
DiceOptim, testthat |
| Published: |
2022-01-23 |
| DOI: |
10.32614/CRAN.package.GPGame |
| Author: |
Victor Picheny 
[aut, cre],
Mickael Binois [aut] |
| Maintainer: |
Victor Picheny <victor.picheny at inra.fr> |
| BugReports: |
https://github.com/vpicheny/GPGame/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/vpicheny/GPGame |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
GPGame results |
Documentation:
Downloads:
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https://CRAN.R-project.org/package=GPGame
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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