A seemingly parallel development is that of synthetic biology, in which cells are engineered to produce target molecules. Synthetic biology is increasingly drawing attention of organic chemists for two reasons: (i) it can improve routes redox efficiency by finding metabolic shortcuts for the key synthetic steps, and (ii) most enzymatic reactions are highly selective. In addition, biochemical reactions are performed under mild operating conditions and usually with benign solvents, which may lower operational costs and reduce life cycle impact of syntheses. Similar to the reaction network of organic synthesis, a map to visualise synthetic biology space has been summarised to guide the biosynthetic planning. A number of pharmaceutical ingredients and bulk chemicals have been produced economically through metabolic engineering approaches. With the knowledge of molecular transformations from organic chemistry and synthetic biology, retrosynthesis relies on multi-step decision making to select optimal reaction routes among all feasible molecular transformations based on criteria such as exergetic efficiency, Efactor, and etc. In linear reaction routes, which include only one-to-one (reactant(s)-product only) 'wiring' (using the networks' jargon) of the reactions, the decision making could be done through exhaustive search of all possible reaction routes, and ranking of the routes based on a set of pre-defined criteria. However, in a topological-tree-styled reaction routes, which include multiple-to-multiple wiring reactions (including co-reactants and bi-products), the number of routes options increase exponentially with the increase in the number of synthetic branches and depth; exhaustive search becomes computationally expensive. In order to improve the efficiency of route design the machine learning method of reinforcement learning has been proposed for the application in synthesis planning. Reinforcement learning (RL) mimics how an intelligent 'decision maker' takes multi-step actions within a specific problem environment to maximise/minimise the cumulative rewards/penalties of the actions. In synthetic planning, the selection of each reaction step within a path is a decision making step. With the given rules and criteria costs, a 'decision maker' starts synthetic planning by trail-and-error, and algorithmically learns from the simulated experience to perform better in next iteration (an 'episode' within the RL jargon).