From 87672f0a8f2693014119bd90766c79e3378e1bdd Mon Sep 17 00:00:00 2001 From: Johannes Czech Date: Thu, 26 Aug 2021 14:06:49 +0200 Subject: [PATCH] Updated README updated links for release 0.9.5 adjusted publication links --- README.md | 49 +++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 41 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index e4b0daa7..6650d6ab 100644 --- a/README.md +++ b/README.md @@ -74,19 +74,18 @@ We provide binary releases for the following plattforms: Operating System | Backend | Compatible with --- | --- | --- -Linux | [**CUDA 11.2, cuDNN 8.1.1.33, TensorRT-7.2.3.4**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.0/CrazyAra_ClassicAra_0.9.0_Linux_TensorRT.zip) | NVIDIA GPUs -Linux | [**MXNet 1.8.0, Intel oneAPI MKL 2021.2.0**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.0/CrazyAra_ClassicAra_0.9.0_Linux_MKL.zip) | Intel CPUs -Windows | [**CUDA 11.2, cuDNN 8.1.1.33, TensorRT-7.2.3.4**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.0/CrazyAra_ClassicAra_0.9.0_Win_TensorRT.zip) | NVIDIA GPUs -Windows | [**MXNet-20190919, Intel MKL 20190502**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.0/CrazyAra_ClassicAra_0.9.0_Win_MKL.zip ) | Intel CPUs -Mac | [**MXNet 1.8.0, Intel oneAPI MKL 2021.2.0**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.0/CrazyAra_ClassicAra_0.9.0_Mac_MKL_post1.zip) | Mac-Books +Linux | [**CUDA 11.3, cuDNN 8.2.1, TensorRT-8.0.1**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.5/CrazyAra_ClassicAra_MultiAra_0.9.5_Linux_TensorRT.zip) | NVIDIA GPUs +Linux | [**MXNet 1.8.0, Intel oneAPI MKL 2021.2.0**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.5/CrazyAra_ClassicAra_MultiAra_0.9.5_Linux_MKL.zip) | Intel CPUs +Windows | [**CUDA 11.3, cuDNN 8.2.1, TensorRT-8.0.1**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.5/CrazyAra_ClassicAra_MultiAra_0.9.5_Win_TensorRT.zip) | NVIDIA GPUs +Windows | [**MXNet 1.8.0, Intel oneAPI MKL 2021.2.0**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.5/CrazyAra_ClassicAra_MultiAra_0.9.5_Win_MKL.zip ) | Intel CPUs +Mac | [**MXNet 1.8.0, Intel oneAPI MKL 2021.2.0**](https://github.com/QueensGambit/CrazyAra/releases/download/0.9.5/CrazyAra_ClassicAra_MultiAra_0.9.5_Mac_MKL_post1.zip) | Mac-Books The current _CrazyAra_ release and all its previous versions can also be found at [releases](https://github.com/QueensGambit/CrazyAra/releases). ### Models -The extracted model should be placed in the same directory as the engine executable. -The directory can be changed by adjusting the UCI-parameter `Model_Directory`. -A default model is included for [**releases >= 0.8.0**](https://github.com/QueensGambit/CrazyAra/releases/). +The extracted model should be placed in the directory reltative to the engine executable. +The default directory is indicated and can be changed by adjusting the UCI-parameter `Model_Directory`. More information about the different models can be found in the [wiki](https://github.com/QueensGambit/CrazyAra-Engine/wiki/Model-description). @@ -207,6 +206,22 @@ For details about the GPL v3 license, refer to the file [LICENSE](https://github } ``` +## M.Sc. Thesis + +* M. Gehrke: **Assessing Popular Chess Variants Using Deep Reinforcement Learning**, [pdf](https://ml-research.github.io/papers/gehrke2021assessing.pdf) +```latex +@mastersthesis{gehrke2021assessing, + title = { Assessing Popular Chess Variants Using Deep Reinforcement Learning }, + author = { Maximilian Alexander Gehrke }, + year = { 2021 }, + type = { M.Sc. }, + crossref = { https://github.com/QueensGambit/CrazyAra }, + school = { TU Darmstadt }, + pages = { 94 }, + month = { jul } + } +``` + * J. Czech: **Deep Reinforcement Learning for Crazyhouse**, [pdf](https://ml-research.github.io/papers/czech2019deep.pdf) ```latex @mastersthesis{czech2019deep, @@ -220,3 +235,21 @@ For details about the GPL v3 license, refer to the file [LICENSE](https://github month = { dec } } ``` + + + +## B.Sc. Thesis + +* M. Langer: **Evaluation of Monte-Carlo Tree Search for Xiangqi**, [pdf](https://ml-research.github.io/papers/langer2021xiangqi.pdf) +```latex +@bachelorthesis{langer2021eval, + title = { Evaluation of Monte-Carlo Tree Search for Xiangqi }, + author = { Maximilian Langer }, + year = { 2021 }, + type = { B.Sc. }, + crossref = { https://github.com/QueensGambit/CrazyAra }, + school = { TU Darmstadt }, + pages = { 45 }, + month = { apr } + } +```