Rethinking Adversarial Inverse Reinforcement Learning: From the Angles of Policy Imitation and Transferable Reward Recovery

In scenarios of inverse reinforcement learning (IRL) with a single expert, adversarial inverse reinforcement learning (AIRL) serves as a foundational approach to providing comprehensive and transferable task descriptions by restricting the reward class, e.g., to state-only rewards. However, AIRL faces practical challenges, primarily stemming from the difficulty of verifying the unobservable transition matrix - often encountered in practice - under the specific conditions necessary for effective transfer. This paper reexamines AIRL in light of the unobservable transition matrix or limited informative priors. By applying random matrix theory (RMT), we demonstrate that AIRL can disentangle rewards for effective transfer with high probability, irrespective of specific conditions. This perspective reframes inadequate transfer in certain contexts. Specifically, it is attributed to the selection problem of the reinforcement learning algorithm employed by AIRL, which is characterized by training variance. Based on this insight, we propose a hybrid framework that integrates on-policy proximal policy optimization (PPO) in the source environment with off-policy soft actor-critic (SAC) in the target environment, leading to significant improvements in reward transfer effectiveness.