/grl

Generic Reinforcement Learning Library
visualization c-plus-plus reinforcement-learning declarative-config yaml-configuration python-gui
  1. C++ 77.0%
  2. C 7.7%
  3. Python 7.5%
  4. Lua 4.3%
  5. CMake 2.8%
  6. MATLAB 0.3%
  7. Other 0.4%
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addons Added Gym example with GRL agent Dec 19, 2018
base Fixed worker not receiving long configurations Dec 19, 2018
bin Added reconfiguration to grlpy Oct 28, 2018
cfg Added experience replay DPG configuration Dec 14, 2018
doc Merge branch 'ikoryakovskiy-master' Mar 3, 2017
externals Fixed grlpy compilation error and segfault Oct 28, 2018
share Syntax highlighting for new Joe version Oct 3, 2018
tests clone() Dec 8, 2016
.gitignore TargetRepresentation now supports gradual changes (0 < interval < 1) Jul 21, 2017
CMakeLists.txt Added Python binding for Environment and Agent (cleanup still needs w… Oct 23, 2018
LICENSE Initial commit Jan 15, 2015
README.md Updated README to describe tensorflow addon installation Apr 9, 2018
doxygen.conf More documentation Jun 25, 2015
grl.cmake Support out-of-tree builds Jan 29, 2016
grl.rosinstall Updated documentation Jun 11, 2015
package.xml grl works fine alongside mprl in catkin workspace Feb 6, 2015

README.md

grl

Generic Reinforcement Learning Library

Copyright 2015 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 http://www.gnu.org/licenses/.

Introduction

GRL is a C++ reinforcement learning library that aims to easily allow evaluating different algorithms through a declarative configuration interface.

Configurator

Installation (Ubuntu 16.04)

sudo apt-get install git cmake g++ libeigen3-dev
git clone https://github.com/wcaarls/grl.git

For the visualization, additionally install

sudo apt-get install libgl1-mesa-dev freeglut3-dev libz-dev

For the odesim environment, additionally install

sudo apt-get install libqt4-opengl-dev libtinyxml-dev libmuparser-dev libode-dev

For the RBDL environment and MUSCOD integration, additionally install

sudo apt-get install liblua5.1-dev

For the configurator, additionally install

sudo apt-get install python-yaml python-tk

For the zeromq addon, additionally install

sudo apt-get install libprotoc-dev protobuf-compiler libsodium-dev

Then download a recent (> 4.0.0) version of zeromq and run './configure, make, make install'

For the tensorflow addon, additionall install

sudo apt-get install libprotoc-dev protobuf-compiler

Then download TensorFlow for C from https://www.tensorflow.org/install/install_c in a place that can be found by the compiler and runtime environment.

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)

Visualizations

Further reading

See grl.pdf