  Recently, reinforcement learning has been used to address logic synthesis by
formulating the operator sequence optimization problem as a Markov decision
process. However, through extensive experiments, we find out that the learned
policy makes decisions independent from the circuit features (i.e., states) and
yields an operator sequence that is permutation invariant to some extent in
terms of operators. Based on these findings, we develop a new RL-based method
that can automatically recognize critical operators and generate common
operator sequences generalizable to unseen circuits. Our algorithm is verified
on both the EPFL benchmark, a private dataset and a circuit at industrial
scale. Experimental results demonstrate that it achieves a good balance among
delay, area and runtime, and is practical for industrial usage.
