rl-satton: Collection of Reinforcement Learning algorithms

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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.

Modules

[Last Documentation]

Control
- Monad
  - Control.Monad.Rnd
Graphics
- Graphics.TinyPlot
RL
- RL.DP
- RL.Imports
- RL.MC
- RL.TD
- RL.TDl
- RL.Types
- RL.Utils

Downloads

rl-satton-0.1.1.tar.gz [browse] (Cabal source package)
Package description (as included in the package)

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Package maintainers

SergeyMironov

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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 && <4.9), binary, containers, deepseq, directory, filepath, free, hashable, heredocs, lens, mersenne-random-pure64, monad-loops, MonadRandom, mtl, pretty-show, process, random, rl-satton, stm, 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 2016-09-20T10:40:37Z
Distributions
Executables	example
Downloads	3829 total (29 in the last 30 days)
Rating	(no votes yet) [estimated by Bayesian average]
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Status	Docs not available [build log] All reported builds failed as of 2016-11-19 [all 3 reports]