In this way, for different datasets, the reward functions can be automatically tuned to approximate the optimal performance, eliminating extra reward engineering and manual interventions.
to enable the policy-based agent to find more diverse evidential paths for predictions, we propose a path-based GAIL method, which can learn the reasoning policy by imitating the path-level semantic features of the demonstrations.