In the paper, we adopt the Proximal Policy Optimization (PPO) algorithm [36] to train our agent. Here we provide details of our algorithm in terms of pseudo code, as shown in Algorithm 1. Similar In this section, we show how the baseline PDRs compute the priority index for the operations. Here we present the complete results on Taillard's benchmark. In Table S.1, we report the results of In Table S.2, we report the generalization performance of our polices trained on The "UB" column is the best solution from The "UB" column is the best solution from Similar conclusion can be drawn from results on DMU benchmark. In Table S.3, we report results In Table S.4 which focuses on The "UB" column is the best solution from The "UB" column is the best solution from We show training curves for all problems in Figure.1.