SHAC++

This repository contains the code for the titled "SHAC++: A Neural Network to Rule All Differentiable Simulators".

This repository contains the code for the ECAI2025 submission titled "SHAC++: A Neural Network to Rule All Differentiable Simulators".

tldr

We present SHAC++, an extension of the SHAC reinforcement learning algorithm. Unlike SHAC, which relies on differentiable environments for backpropagation through time, SHAC++ leverages neural networks trained alongside the policy to model the environment.

Useful Resources:

• Web Page: link
• Paper: link

Pre-requisites

The prerequisites are contained in the file requirements.txt. You can install them by running pip install -r requirements.txt. However, we suggest using a virtual environment to avoid conflicts with other packages.

python3 -m venv venv
source venv/bin/activate
python3 -m pip install -r requirements.txt

Execution

The experiments are all generated in the makefile makefile.mk. You can simply run all experiments by using the command make -f makefile.mk all. The directory data will be created and populated with models, logs, and checkpoints.

BibTeX

@inproceedings{shacpp,
  title={SHAC++: A Neural Network to Rule All Differentiable Simulators},
  author={Bertolotti, Francesco and Aguzzi, Gianluca and Cazzola, Walter and Viroli, Mirko},
  booktitle={European Conference on Artificial Intelligence (ECAI)},
  year={2025},
  url={https://github.com/f14-bertolotti/shacpp}
}