DIANA-Gym is a framework for training Reinforcement Learning agents for autonomous navigation tasks of rovers intended for planetary exploration. It is based upon several open-source projects and libraries, such as ROS2, ros2learn and Gym-Gazebo2 from Acutronic Robotics, PIC4rl_gym from Pic4Ser, Stable Baselines3, OpenAI Gym and Gazebo.
It enables the training and monitoring of Reinforcement Learning agents in simulated environments, in particular, it features the simulated Marsyard from European Rover Challenge 23 as default environment. The framework is designed to be modular and extensible, allowing the user to easily add new environments, algorithms and robots, as well as to easily extend it to different robotic tasks.
DIANA-Gym is being developed and maintained by DIANA, a student team from Politecnico di Torino.
Please note that this project is still under development and is to be considered in Beta testing. Fixes and additional features are planned.
Please refer to the ros2learn installation guide to install the required dependencies.
Other useful packages are Stable Baselines3 and Wandb, which can be installed with pip3 install stable-baselines3 wandb.
Once the dependencies are installed, edit the value of the MARA_PATH variable in environments/gym-gazebo2/gym_gazebo2/utils/ut_launch.py to the path of your workspace built while following this guide. Finally, edit accordingly also the bash script at environments/gym-gazebo2/provision/mara_setup.sh.
An example script is available at experiments/examples/DIANA/train_test.py
Run python train_test.py -g to train an agent using the default configuration with the visual interface.
This work has been presented at IAC23, paper available at https://iafastro.directory/iac/paper/id/80146/ext/appendix/IAC-23,D1,2,9,x80146.pdf
@misc{
Author = {Federico Mustich, Leonardo Maria Festa, Fabrizio Stesina, Raffaello Camoriano, Gabriele Tiboni},
Title = {Point Cloud-Based Reinforcement Learning for Autonomous Navigation of a Robotic Rover on Planetary
Surfaces},
Year = {2023},
}
Stay DIANA, Stay Mambo.