rl-satton: Collection of Reinforcement Learning algorithms
rl-satton provides implementation of algorithms, described in the 'Reinforcement Learing: An Introduction' book by Richard S. Satton and Andrew G. Barto. In particular, TD(0), TD(lambda), Q-learing are implemented. Code readability was placed above performance. Usage examples are provided in the ./examples folder.
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- rl-satton-0.1.2.4.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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| Versions [RSS] | 0.1.0, 0.1.1, 0.1.2, 0.1.2.1, 0.1.2.2, 0.1.2.3, 0.1.2.4 |
|---|---|
| Dependencies | base (>=4.8 && <5), containers, directory, filepath, hashable, lens, mersenne-random-pure64, monad-loops, MonadRandom, mtl, parsec, pretty-show, process, random, rl-satton, stm, template-haskell, text, time, transformers, unordered-containers [details] |
| License | BSD-3-Clause |
| Copyright | Copyright (c) 2016, Sergey Mironov |
| Author | Sergey Mironov |
| Maintainer | grrwlf@gmail.com |
| Category | Machine Learning |
| Home page | https://github.com/grwlf/rl |
| Uploaded | by SergeyMironov at 2016-10-04T09:06:15Z |
| Distributions | |
| Executables | example |
| Downloads | 3840 total (11 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
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| Status | Docs available [build log] Last success reported on 2016-10-04 [all 1 reports] |