Reinforcement learning (RL) is a framework where agents learn to make decisions by interacting with an environment in order to maximise cumulative rewards. In the context of TS geometry prediction, the problem has been reformulated as an RL task in two studies. Zhang et al. approached this task by conceptualising it as a "shooting game" related to transition path sampling. In this framework, an agent employs Monte Carlo techniques to propose a hypothetical TS structure. From this proposed structure, two trajectories with opposite momenta are projected, and a reward is calculated based on the distance between these trajectories and the target reactant and product states. The agent then uses this feedback to refine its guesses for the TS geometry. Although this method generated plausible TS structures, the accuracy of these structures compared to DFT-optimised TSs was not reported, leaving the quantitative performance of the approach unclear.