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.

翻译：本文构建了一个利用强化学习进行化学发现的模拟实验室。由于强化学习对数据需求较高，在现实世界中通过直接执行动作来实时训练智能体既不可行，又可能带来安全隐患。此外，化学加工与发现过程涉及强化学习基准测试中不常见的挑战，因此为研究提供了广阔空间。我们基于标准Open AI Gym模板，推出了一套高度可定制化的开源强化学习环境——ChemGymRL。该框架支持一系列相互关联的虚拟化学实验台，使强化学习智能体能够在此进行操作与训练。本文以经典化学反应为示例，逐一介绍并详述各实验台的构造，同时在各实验台上训练了多种标准强化学习算法。最后，本文对若干标准强化学习方法的性能进行了讨论与比较，并提出了未来工作的方向，作为ChemGymRL进一步开发与应用的远景规划。