This is the source code of two simple navigation experiments: an obstacle avoidance and a maze navigation. Neural networks are generated thanks to the sferes framework to solve these tasks. Several different fitness functions are provided for comparison as well as process helpers like behavioral diversity or novelty.
These experiments have illustrated tutorials given by Stephane Doncieux on selective pressures. This was part of a larger tutorial covering different topics in evolutionary robotics and given by Nicolas Bredeche, Jean-Baptiste Mouret and Stephane Doncieux at:
- Artificial Life conference, New York, 2014
- GECCO, Madrid, 2015
- ECAL, York 2015
A Docker image in which all the dependencies have been installed together with sferes the navigation experiment and the required module can be found here: https://hub.docker.com/r/doncieux/navigation/ To use it, you need to install docker (https://www.docker.com/) and to do the following: docker pull doncieux/navigation To run the docker image: docker run -it doncieux/navigation
The navigation executables are in /git/sferes2/build/exp/navigation
You can launch them or make your own modifications and test their result.
WARNING: the modifications you make in the container will be lost when you quit the image. If you want to keep it, you need to create data volumes or mount a host directory. See https://docs.docker.com/engine/tutorials/dockervolumes/ for more details.
sferes2 core (https://github.com/sferes2/sferes2) sferes2 modules:
- fastsim (https://github.com/jbmouret/libfastsim for the core library and https://github.com/sferes2/fastsim to connect it to sferes)
- nn2 (https://github.com/sferes2/nn2)
sferes2 has its own dependencies (notably boost, tbb and eigen). Check sferes2 documentation for more details.
In the following, it is assumed that all the git repositories have been cloned in ~/git.
First, libfastsim need to be compiled: cd ~/git/libfastsim ./waf configure ./waf build
Before compiling the experiment, the following instructions need to be executed: cd ~/git/sferes2/modules ln -s ~/git/fastsim ln -s ~/git/nn2 cd ~/git/sferes2/exp ln -s ~/git/navigation cd ~/git/sferes2/ echo fastsim >> modules.conf echo nn2 >> modules.conf
To compile the experiment: cd ~/git/sferes2 ./waf configure ./waf build --exp=navigation
To run an experiment, launch one of the executables in ~/git/sferes2/build/default/exp/navigation. It will create a directory with the generated results. gen_XX files contain the best individual(s) of the generation XX. To look at its behavior, launch the following command: my_executable -o data --load=gen_XX where my_executable is the the program that has generated gen_XX and XX the generation number to look at.
An experiment describes what is to be evolved, how and how it is evaluated. The experiments provided here contain mostly the evaluation part (the fitness function). The other parts are standard sferes2 modules.
Variants of an experiments are defined using macros. This is the reason why the source conde contains many #ifdef VARIANT1 // what to do for VARIANT1 #else // what to do for other variants #endif
A variant of an experiment is defined by a set of labels. The choice of the variants to compile is made in the wscript file. The variant with the labels VARIANT1 FIT1 ENV1 of an exp my_exp.cpp creates an executable called my_exp_variant1_fit1_env1.
Simple experiment in which a two wheeled robot has to avoid obstacles. Several fitness functions have been defined: FIT1, FIT2 and FIT3 to illustrate the impact of their definition on the results (don't expect much fom FIT1...). Likewise, three different environment of increasing difficulty have been defined: ENVOA1, ENVOA2 and ENVOA3.
Experiment in which the robot has to find how to go out of a maze. The variants are the fitness: FITDIST telle to take into account the distance to the output, otherwise it is a binary fitness function (success or failure). The DIVERSITY variant includes a behavioral diversity process helper and the NOVELTY variant includes a novelty search objective. Without any of them, it does not work at all, with the DIVERSITY, some experiment do converge, with NOVELTY, most of them do.
- Stephane Doncieux [email protected]: main author and maintainer
- Jean-Baptiste Mouret
See sferes2 main documentation for an overview of this software framework.
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