https://github.com/wcaarls/grl
Generic Reinforcement Learning Library
https://github.com/wcaarls/grl
c-plus-plus declarative-config python-gui reinforcement-learning visualization yaml-configuration
Last synced: 8 months ago
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Generic Reinforcement Learning Library
- Host: GitHub
- URL: https://github.com/wcaarls/grl
- Owner: wcaarls
- License: lgpl-3.0
- Created: 2015-01-15T17:45:32.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2024-12-04T19:49:34.000Z (about 1 year ago)
- Last Synced: 2025-04-28T18:08:41.024Z (8 months ago)
- Topics: c-plus-plus, declarative-config, python-gui, reinforcement-learning, visualization, yaml-configuration
- Language: C++
- Size: 14.2 MB
- Stars: 9
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# grl
[](https://github.com/wcaarls/grl/actions?query=workflow%3ACMake)
Generic Reinforcement Learning Library
Copyright 2015-2022 Wouter Caarls
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
# Introduction
GRL is a C++ reinforcement learning library that aims to easily allow
evaluating different algorithms through a declarative configuration
interface.
# Installation
## Ubuntu 22.04
```
sudo apt install git cmake g++ libeigen3-dev libpython3-dev python3-distutils libz-dev
git clone https://github.com/wcaarls/grl.git
```
### For the visualization, additionally install
```
sudo apt-get install libgl1-mesa-dev freeglut3-dev
```
### For the configurator, additionally install
```
sudo apt-get install python3-yaml python3-tk
```
### For the tensorflow addon, additionall install
```
sudo apt-get install libprotobuf-dev protobuf-compiler
# Tensorflow C API 2.8.0
wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz
sudo tar zxvf libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz -C /usr/local
# CUDA Toolkit 11.7
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
sudo apt-get update
sudo apt-get install cuda
### cuDNN 8.2.1
wget https://anaconda.org/anaconda/cudnn/8.2.1/download/linux-64/cudnn-8.2.1-cuda11.3_0.tar.bz2
sudo tar -jxvf cudnn-8.2.1-cuda11.3_0.tar.bz2 -C /usr/local --wildcards "lib/*"
```
Then edit `~/.bashrc` to include the following, and open a new terminal
```
export PATH=$PATH:/usr/local/cuda-11/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda-11/lib64
```
Install Tensorflow 2 for Python
```
sudo -H python3 -m pip install tensorflow
```
If there are errors relating to Python loading
`libtensorflow_framework.so.2`, it is because it comes with its own version,
and it finds the version from the C API. Workaround:
```
sudo apt-get install patchelf
sudo patchelf --replace-needed libtensorflow_framework.so.2 libtensorflow_framework.so.2.8.0 /usr/local/lib/libtensorflow.so.2.8.0
sudo rm /usr/local/lib/libtensorflow_framework.so /usr/local/lib/libtensorflow_framework.so.2
```
# Setup
```
mkdir build
cd build
cmake ..
make
```
# Running
To directly perform an experiment, run
```
./grld ../cfg/pendulum/sarsa_tc.yaml
```
To start the configurator instead, run
```
cd ../bin
./grlc ../cfg/pendulum/sarsa_tc.yaml
```
# Visualizations
GRL comes with standard visualizations for value functions,
policies, and the integrated environments (e.g. pendulum
swing-up, cart-pole swing-up, compass walker)
# Further reading
See [grl.pdf](/doc/grl.pdf)