Augmented Hill-Climb 30 (AHC) modifies REINVENT such that only the top-k ranked molecules in a batch are used to update the network weights using Equation 2 which drastically improves the learning efficiency of the algorithm. Thus, in this work we used AHC to conduct RL with 𝜎 = 60, 𝑘 = 0.5, 𝑏𝑎𝑡𝑐ℎ 𝑠𝑖𝑧𝑒 = 64 which was conducted for 200 steps resulting in 12,800 molecules sampled by the agent network.