Goal-directed Molecular Design with Policy-based Reinforcement Learning. Molecular generation can be framed as a policy-based RL problem, where a base model (Prior) is trained on a general dataset and fine-tuned (Agent) to generate molecules with desired property profiles. Existing works that follow this paradigm include SMILES-based RNNs , generative adversarial networks (GANs) , variational autoencoders (VAEs) , graph-based models , and GFlowNets .