  Reinforcement Learning (RL) has demonstrated substantial potential across
diverse fields, yet understanding its decision-making process, especially in
real-world scenarios where rationality and safety are paramount, is an ongoing
challenge. This paper delves in to Explainable RL (XRL), a subfield of
Explainable AI (XAI) aimed at unravelling the complexities of RL models. Our
focus rests on state-explaining techniques, a crucial subset within XRL
methods, as they reveal the underlying factors influencing an agent's actions
at any given time. Despite their significant role, the lack of a unified
evaluation framework hinders assessment of their accuracy and effectiveness. To
address this, we introduce XRL-Bench, a unified standardized benchmark tailored
for the evaluation and comparison of XRL methods, encompassing three main
modules: standard RL environments, explainers based on state importance, and
standard evaluators. XRL-Bench supports both tabular and image data for state
explanation. We also propose TabularSHAP, an innovative and competitive XRL
method. We demonstrate the practical utility of TabularSHAP in real-world
online gaming services and offer an open-source benchmark platform for the
straightforward implementation and evaluation of XRL methods. Our contributions
facilitate the continued progression of XRL technology.
