This code implements a simulated agent in 2D environment with box2d physics. The agent is a 3-DoF arm with a 2-DoF gripper. The environment is also filled with an object. The agent is controlled by a computational model which learns a map of its sensorimotor contingencies via the interaction with the environment. The model is described in the paper: Mannella F, Tummolini L. 2022 Kick-starting concept formation with intrinsically motivated learning: the grounding by competence acquisition hypothesis. Phil. Trans. R. Soc. B 20210370. https://doi.org/10.1098/rstb.2021.0370.
The code relies on tensorflow, although it can be run also without the gpu enhancement. Other required libraries are:
- gym
- box2d
You must first install the box2dsim packege to run the simultor:
pip install -e tools/box2dsim
You also must decompress the data archive in the source folder
cd src
tar xzvf data.tar.gz
To start a simulation copy the src folder and run SMMain.py form the copied folder.
python SMMain.py -h
usage: SMMain.py [-h] [-t TIME] [-g] [-s SEED] [-x]
optional arguments:
-h, --help show this help message and exit
-t TIME, --time TIME The maximum time for the simulation (seconds)
-g, --gpu Use gpu
-s SEED, --seed SEED Simulation seed
-x, --plots Plot graphs
All parameters for a simulation (included the number of epochs to be run) are described in SMMain.py. Modify the params.py file in the copied src folder if you need to tweek the parameters of a simulation.
Example:
python SMMain.py -s 1000 -t 14400 -g -x
runs a simulation lasting 4 hours with graphics swithed on and using the gpu. If the simulation does not end in the required time, all data are recorded in the folder and the simulation can be restarted from the current time just by running again the script.
Shield:
This work is licensed under a Creative Commons Attribution 4.0 International License.
