Reinforcement machine learning is implemented to survey a series of model potential energy surfaces and ultimately identify the global minima point. Through sophisticated reward function design, the introduction of an optimizing target, and incorporating physically motivated actions, the reinforcement learning agent is capable of demonstrating advanced decision making. These improved actions allow the agent to successfully converge to an optimal solution more rapidly when compared to an agent trained without the aforementioned modifications. This study showcases the conceptual feasibility of using reinforcement machine learning to solve difficult environments, namely, potential energy surfaces, with multiple, seemingly, correct solutions in the form of local minima regions. Through these results, we hope to encourage extending reinforcement learning to more complicated optimization problems and using these novel techniques to efficiently solve traditionally challenging problems in chemistry.

中文翻译：

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使用强化机器学习探索势能面

实施强化机器学习来调查一系列模型势能面并最终确定全局最小值点。通过复杂的奖励函数设计、优化目标的引入以及结合身体动机的行为，强化学习代理能够展示高级决策。与未经上述修改训练的代理相比，这些改进的动作允许代理更快地成功收敛到最佳解决方案。本研究展示了使用强化机器学习解决困难环境（即势能面）的概念可行性，并以局部最小区域的形式具有多个看似正确的解决方案。通过这些结果，