Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
| Version: | 1.0.5 |
| Depends: | R (≥ 3.2.0) |
| Imports: | ggplot2, hash (≥ 2.0), data.table |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2020-03-02 |
| DOI: | 10.32614/CRAN.package.ReinforcementLearning |
| Author: | Nicolas Proellochs [aut, cre], Stefan Feuerriegel [aut] |
| Maintainer: | Nicolas Proellochs <nicolas.proellochs at wi.jlug.de> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | ReinforcementLearning results |
| Reference manual: | ReinforcementLearning.pdf |
| Vignettes: |
Reinforcement Learning in R |
| Package source: | ReinforcementLearning_1.0.5.tar.gz |
| Windows binaries: | r-devel: ReinforcementLearning_1.0.5.zip, r-release: ReinforcementLearning_1.0.5.zip, r-oldrel: ReinforcementLearning_1.0.5.zip |
| macOS binaries: | r-release (arm64): ReinforcementLearning_1.0.5.tgz, r-oldrel (arm64): ReinforcementLearning_1.0.5.tgz, r-release (x86_64): ReinforcementLearning_1.0.5.tgz, r-oldrel (x86_64): ReinforcementLearning_1.0.5.tgz |
| Old sources: | ReinforcementLearning archive |
| Reverse imports: | lazytrade |
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