Simple-Playgrounds (SPG) is an easy-to-use, fast and flexible simulation environment for research in Deep Reinforcement Learning and Artificial Intelligence. This simulator proposes a huge diversity of environments for embodied agents learning through physical interactions. It bridges the gap between simple and efficient grid environments, and complex and challenging 3D environments.
The playgrounds are 2D environments where agents can move around and interact with scene elements. The game engine, based on Pymunk and Pygame, deals with simple physics, such as collision and friction. Agents can act through continuous movements and discrete interactive actions. They perceive the scene with realistic first-person view sensors, top-down view sensors, and semantic sensors.
Simple PLaygrounds follows the gymnasium environment definitions.
This simulator is easy to handle, and very flexible. It allows very fast design of AI experiments and runs experiments very quickly.
We hope that you can make use of this simulator for your research. If you publish your work based on this simulator, please use the following reference:
@misc{Simple-Playgrounds,
author = {Garcia Ortiz, Michael and Jankovics, Vince and Caselles-Dupre, Hugo and Annabi, Louis},
title = {Simple-Playgrounds},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/mgarciaortiz/simple-playgrounds}},
}
The first version of the simulator was called Flatland, and was designed by Hugo Caselles-Dupre, Louis Annabi, Oksana Hagen and Michael Garcia Ortiz.
The new version was developed by Vince Jankovics, Hugo Caselles-Dupre, Louis Annabi and Michael Garcia Ortiz.
We would like to thank Clement Moulin-Frier and Younes Rabii for their helpful suggestions and their contributions to the new version of the simulator.