Reinforcement learning agents learn to make optimal decisions given information about a problem. These agents consist of a policy, which takes in a state (e.g., previously tried reaction conditions and corresponding yields) and suggests an action (e.g., a new set of reaction conditions). The optimality of an action or a set of actions is quantified through a reward. Zhou et al. built the bridge from RL to optimizing chemical reactions: an algorithm that (decision-maker) sequentially suggests experiments to carry out (actions) resulting in the highest yield (reward). Thereby, they proposed the Deep Reaction optimiser (DRO), a recurrent neural network architecture with a RL-motivated loss function that is proportional to the yield obtained from each experiment suggested and carried out. It was assumed that the response surfaces for chemical reactions are continuous and can be approximated by Gaussian processes (GPs). Thus, the DRO was pretrained on GPs in order to simulate optimising the response surfaces of chemical reactions. These pretrained models were then inferred to yield optimisation of actual chemical reactions. Figure illustrates DRO applied to a two dimensional problem.