XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
<|reference_start|>XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques: 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.<|reference_end|>
@article{xiong2024xrl-bench:,
    title={XRL-Bench: A Benchmark for Evaluating and Comparing Explainable
  Reinforcement Learning Techniques},
    author={Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu, Kai Guan, Xingchen Fang, Ji
  Jiang, Tianze Zhou, Yujing Hu, Haoyu Liu, Tangjie Lyu, Changjie Fan},
    journal={arXiv preprint arXiv:2402.12685},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12685},
    primaryClass={cs.AI}
}