evorobotpy2

Setup

First, if you are using a Debian-based Linux, install the following packages:

sudo apt-get install libgsl-dev libopenmpi-dev python3-virtualenv python3-dev python3-tk make g++

After that, create a virtual environment in the root directory:

virtualenv -p python3 venv
source venv/bin/activate

Then install the requirements:

pip install -r requirements.txt

To make things easier you can create an alias to run the simulator:

make create_alias

And now we are good to go!

Compiling

Before running the models, you need to compile the resources that you'll use. The main resources available are: ErDiscrim, ErDpole, ErPredprey, ErStaybehind and Evonet. You can look for the compile command in your terminal by writing make compile_ and then pressing tab to see all commands. You can find all of them in the Makefile, but here's a list:

make compile_erdiscrim
make compile_erdpole
make compile_evonet
make compile_erpredprey
make compile_erstaybehind

And if you just want to install all of them at once:

make compile_all

Running

To run a model, first go to the target environment, and then run the following command:

evrun -f {target environment}.ini

or

python3 ~/evorobotpy2/bin/es.py -f {target environment}.ini

To see all execution options run:

evrun --help

Contributing

If at some point you installed a new package, and therefore it will be needed to run the new code, you can update the requirements by running:

make update_requirements

Concepts

A tool for training robots through evolutionary and reinforcement learning methods

The tool is documented in How to Train Robots through Evolutionary and Reinforcement Learning Methods which includes also a detailed tutorial with exercises.

For an introduction to the theory and to state-of-the-art research in this areas, see the associated open-access book Behavioral and Cognitive Robotics: An Adaptive Perspective

Credits

Please use this BibTeX to cite this repository in your publications:

@misc{evorobotpy2,
  author = {Stefano Nolfi, Brenda S. Machado and Arthur H. Bianchini},
  title = {A tool for training robots through evolutionary and reinforcement learning methods},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Brenda-Machado/evorobotpy2/}},
}