We train one RL agent for each latent-space model (VAE-CYC and MolMIM) for 500 epochs and a batch size of 100 with an initial seed of 5 vectors sampled from a Gaussian distribution being the starting states. We additionally apply a restart strategy every 100 epochs to reset the optimization from the top-10 explored states. We additionally run MOLRL for a larger number of epochs showing that running the optimization longer yields better results.