ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

This paper provides a simulated laboratory for making use of reinforcement learning (RL) for material design, synthesis, and discovery. Since RL is fairly data intensive, training agents 'on-the-fly' by taking actions in the real world is infeasible and possibly dangerous. Moreover, chemical processing and discovery involves challenges which are not commonly found in RL benchmarks and therefore offer a rich space to work in. We introduce a set of highly customizable and open-source RL environments, ChemGymRL, implementing the standard gymnasium API. ChemGymRL supports a series of interconnected virtual chemical benches where RL agents can operate and train. The paper introduces and details each of these benches using well-known chemical reactions as illustrative examples, and trains a set of standard RL algorithms in each of these benches. Finally, discussion and comparison of the performances of several standard RL methods are provided in addition to a list of directions for future work as a vision for the further development and usage of ChemGymRL. Demonstration of a new open source Python library for simulating chemistry experiments as a gymnasium-API, reinforcement learning environment. Allowing learning policies for material design tasks or pipelines using a modular, extendable design.