As a simple baseline strategy, we implemented REINFORCE which is also used in . This is an 'all-actions' policy-based method because the policy update only requires a sum over all actions and the return for the whole episode (final molecule)important due to potentially invalid partial smiles during generation. The loss function is described in Equation where it can be interpreted as a scaling of the policy (here the negative log likelihood, also described in Equation ) by the reward given to the complete molecule (𝐑 𝐓 ).