  In recent years, deep learning has made remarkable strides, surpassing human
capabilities in tasks like strategy games, and it has found applications in
complex domains, including protein folding. In the realm of quantum chemistry,
machine learning methods have primarily served as predictive tools or design
aids using generative models, while reinforcement learning remains in its early
stages of exploration. This work introduces an actor-critic reinforcement
learning framework suitable for diverse optimization tasks, such as searching
for molecular structures with specific properties within conformational spaces.
As an example, we show an implementation of this scheme for calculating minimum
energy pathways of a Claisen rearrangement reaction and a number of SN2
reactions. Our results show that the algorithm is able to accurately predict
minimum energy pathways and thus, transition states, therefore providing the
first steps in using actor-critic methods to study chemical reactions.
