  Adversarial inverse reinforcement learning (AIRL) stands as a cornerstone
approach in imitation learning, yet it faces criticisms from prior studies. In
this paper, we rethink AIRL and respond to these criticisms. Criticism 1 lies
in Inadequate Policy Imitation. We show that substituting the built-in
algorithm with soft actor-critic (SAC) during policy updating (requires
multi-iterations) significantly enhances the efficiency of policy imitation.
Criticism 2 lies in Limited Performance in Transferable Reward Recovery Despite
SAC Integration. While we find that SAC indeed exhibits a significant
improvement in policy imitation, it introduces drawbacks to transferable reward
recovery. We prove that the SAC algorithm itself is not feasible to disentangle
the reward function comprehensively during the AIRL training process, and
propose a hybrid framework, PPO-AIRL + SAC, for a satisfactory transfer effect.
Criticism 3 lies in Unsatisfactory Proof from the Perspective of Potential
Equilibrium. We reanalyze it from an algebraic theory perspective.
