@inproceedings{
beeler2023demonstrating,
title={Demonstrating ChemGym{RL}: An Interactive Framework for Reinforcement Learning for Digital Chemistry},
author={Chris Beeler and Sriram Ganapathi Subramanian and Kyle Sprague and Colin Bellinger and Mark Crowley and Isaac Tamblyn},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=cSz69rFRvS}
}
This tutorial describes a simulated laboratory for making use of reinforcement learning (RL) for chemical discovery. A key advantage of the simulated environment is that it enables RL agents to be trained safely and efficiently. In addition, it offer an excellent test-bed for RL in general, with challenges which are uncommon in existing RL benchmarks. The simulated laboratory, denoted  ChemGymRL, is open-source, implemented according to the standard Gymnasium API, and is highly customizable. It supports a series of interconnected virtual chemical \emph{benches} where RL agents can operate and train. Within this tutorial introduce the environment, demonstrate how to train off-the-shelf RL algorithms on the benches, and how to modify the benches by adding additional reactions and other capabilities. In addition, we discuss future directions for ChemGymRL benches and RL for laboratory automation and the discovery of novel synthesis pathways. The software, documentation and tutorials are available here: https://www.chemgymrl.com
Demonstrating ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler Sriram Ganapathi Subramanian Kyle Sprague Colin Bellinger Mark Crowley Isaac Tamblyn