The initial state s 0 is selected either as a fixed starting molecule encoded by the latent model's encoder s 0 = z 0 or seed vectors sampled from a Gaussian distribution representing random points in the latent space. For optimization applications where a starting set of molecules is available and the task is to further improve properties or activity, the first option is chosen. However, when designing molecules with specific activity or properties and no starting point is available, then the initial states are random points in the latent Since we are not concerned with stepwise transitions of the agent, we treat transitions as single final actions, where the maximum number of steps we consider is t = 1 per state. Accordingly, rewards R t are computed as single step rewards ignoring discounted rewards calculation, a practice that is common in policy gradient optimization.