Implements reinforcement learning environments and algorithms as described in Sutton & Barto (1998, ISBN:0262193981). The Q-Learning algorithm can be used with different types of function approximation (tabular and neural network), eligibility traces (Singh & Sutton (1996) <doi:10.1007/BF00114726>) and experience replay (Mnih et al. (2013) <arXiv:1312.5602>).
| Version: | 0.1.0 |
| Depends: | R (≥ 3.0.0) |
| Imports: | checkmate (≥ 1.8.4), R6 (≥ 2.2.2), nnet (≥ 7.3-12), purrr (≥ 0.2.4) |
| Suggests: | reticulate, keras, knitr, rmarkdown, testthat, covr, lintr |
| Published: | 2018-01-03 |
| Author: | Markus Dumke [aut, cre] |
| Maintainer: | Markus Dumke <markusdumke at gmail.com> |
| BugReports: | https://github.com/markusdumke/reinforcelearn/issues |
| License: | MIT + file LICENSE |
| URL: | http://markusdumke.github.io/reinforcelearn |
| NeedsCompilation: | no |
| SystemRequirements: | (Python and gym only required if gym environments are used) |
| Materials: | README NEWS |
| CRAN checks: | reinforcelearn results |
| Reference manual: | reinforcelearn.pdf |
| Vignettes: |
Agents Environments |
| Package source: | reinforcelearn_0.1.0.tar.gz |
| Windows binaries: | r-devel: reinforcelearn_0.1.0.zip, r-release: reinforcelearn_0.1.0.zip, r-oldrel: reinforcelearn_0.1.0.zip |
| OS X El Capitan binaries: | r-release: reinforcelearn_0.1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: reinforcelearn_0.1.0.tgz |
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