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.

该研究提供了一个基于 Open AI Gym 模板的高度可定制和开源的 RL 环境，ChemGymRL，以支持在化学发现中训练 RL 代理，通过使用一系列互连的虚拟化学试验台。研究介绍了这些试验台，并在其中训练了一组标准 RL 算法，最后提供了多个标准 RL 方法的性能讨论和比较以及未来工作的发展方向列表作为 ChemGymRL 的远景。

ChemGymRL: 数字化学强化学习的交互式框架

ChemGymRL: An Interactive Framework for Reinforcement Learning for  Digital Chemistry

In recent years, reinforcement learning (RL) has emerged as a popular
approach for solving sequence-based tasks in machine learning. However, finding
suitable alternatives to RL remains an exciting and innovative research area.
One such alternative that has garnered attention is the Non-Axiomatic Reasoning
System (NARS), which is a general-purpose cognitive reasoning framework. In
this paper, we delve into the potential of NARS as a substitute for RL in
solving sequence-based tasks. To investigate this, we conduct a comparative
analysis of the performance of ONA as an implementation of NARS and
$Q$-Learning in various environments that were created using the Open AI gym.
The environments have different difficulty levels, ranging from simple to
complex. Our results demonstrate that NARS is a promising alternative to RL,
with competitive performance in diverse environments, particularly in
non-deterministic ones.

探讨了非公理推理系统（NARS）作为强化学习（RL）替代品的潜力，并进行了性能对比分析，结果表明 NARS 在不同难度环境中表现竞争力尤其是在非确定性环境中。