ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry

• URL: http://arxiv.org/abs/2305.14177v1
• Date: Tue, 23 May 2023 15:56:17 GMT
• Title: ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
• Authors: Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn
• Abstract summary: This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents on-the-fly' by taking actions in the real world is infeasible and possibly dangerous. We introduce a set of highly customizable and open-source RL environments, ChemGymRL, based on the standard Open AI Gym template.
• Score: 2.350237106287331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical 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, based on the standard Open AI Gym template. 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.

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