Summary of Xrl-bench: a Benchmark For Evaluating and Comparing Explainable Reinforcement Learning Techniques, by Yu Xiong et al.
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
by Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu, Kai Guan, Xingchen Fang, Ji Jiang, Tianze Zhou, Yujing Hu, Haoyu Liu, Tangjie Lyu, Changjie Fan
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Explainable Reinforcement Learning (XRL), a subfield of Explainable AI aimed at understanding the decision-making process of RL models in real-world scenarios. The authors focus on state-explaining techniques, which reveal the underlying factors influencing an agent’s actions. To evaluate and compare XRL methods, they propose XRL-Bench, a unified standardized benchmark with three main modules: standard RL environments, explainers based on state importance, and standard evaluators. The benchmark supports both tabular and image data for state explanation. Additionally, the authors introduce TabularSHAP, an innovative XRL method that demonstrates practical utility in real-world online gaming services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning more understandable and safe. They want to figure out how a computer program makes decisions when it’s playing games or doing other tasks. The program can’t just make random choices because that wouldn’t be fair or smart. The researchers are working on ways to explain why the program made certain moves, which is important for making sure the program behaves well in real-life situations. They created a special tool called XRL-Bench to help evaluate how good these explanations are. This could lead to more reliable and trustworthy AI systems. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
