From 773f117239cc0bd346edb1300b7e403c100eb988 Mon Sep 17 00:00:00 2001 From: Lilian Besson Date: Sun, 2 Apr 2017 14:24:27 +0200 Subject: [PATCH] Better styling --- README.md | 43 ++++++++++++++++++++----------------------- 1 file changed, 20 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 3363db7..b82c116 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ -#LibBandit +# LibBandit -LibBandit is a C++ library designed for efficiently simulating multi-armed bandit algorithms. +LibBandit is a C++ library designed for efficiently simulating [multi-armed bandit](https://en.wikipedia.org/wiki/Multi-armed_bandit) algorithms, by [Tor Lattimore](https://github.com/tor). Currently the following algorithms are implemented: + * UCB -* Optimally confident UCB +* Optimally confident UCB (OCUCB) * Almost optimally confident UCB * Thompson sampling (Gaussian prior) * MOSS @@ -15,38 +16,34 @@ Currently the following algorithms are implemented: Defining new noise models is as simple as extending a base class and implementing the reward function. -##Compiling - -You will need a C++11 compliant compiler such as g++ 4.8 or clang 5. - -LibBandit uses the Scons build system. With this installed you should be able to compile all sources by typing `scons` - - -##Using the Library +## Compiling -LibBandit is easy to use. See the examples/ folder. +You will need a C++11 compliant compiler such as `g++` 4.8 or `clang` 5. +LibBandit uses the [Scons build system](http://scons.org/). With this installed, you should be able to compile all sources by typing `scons` -##Gittins Index -The library includes code for efficiently generating Gittins indices for a Gaussian prior and noise model. Included is a precomputed -table of indices for horizons up to 5,000. See the examples/ folder for details on how to use this data. +## Using the Library -To compute the indices yourself use `makegittins build ` +LibBandit is easy to use. See the [examples/ folder](examples/). -The tolerance should be chosen as small as possible. The pre-computed table used tolerance = 0.000005. -You can lookup the Gittins index in a table with `makegittins lookup ` where is the number of rounds -remaining and is the number of samples from that arm. +## Gittins Index -A larger pre-computed table for horizon 10,000 and tolerance 0.000005 is available for download from http://downloads.tor-lattimore.com/gittins/10000.zip. +The library includes code for efficiently generating Gittins indices for a Gaussian prior and noise model. +Included is a precomputed table of indices for horizons up to `5,000`. See the [examples/ folder](examples/) for details on how to use this data. +To compute the indices yourself use `makegittins build `. +The tolerance should be chosen as small as possible. The pre-computed table used `tolerance = 0.000005`. -##Contributing +You can lookup the Gittins index in a table with `makegittins lookup ` where `` is the number of rounds remaining and `` is the number of samples from that arm. -If you implement a new algorithm please (a) test it against existing algorithms and (b) contact me to become a -contributor so others can easily test against your algorithm. +A *larger* pre-computed table for horizon `10,000` and tolerance `0.000005` is available for download from http://downloads.tor-lattimore.com/gittins/10000.zip. +## Contributing +If you implement a new algorithm, please +1. test it against existing algorithms and +2. contact me to become a contributor so others can easily test against your algorithm.