AI2030 Responsible AI Tools Marketplace

Key Takeaways

The lack of standardized evaluation methods for explainable AI in reinforcement learning has been a significant barrier to progress. XRL-Bench addresses this gap by providing the first unified benchmark framework, enabling researchers and practitioners to fairly compare different explanation techniques on a level playing field.
State-explaining techniques are critical for real-world AI safety and accountability. These methods reveal which factors influence an AI agent's decisions at any given moment, which is essential in high-stakes applications where understanding "why" the AI acted a certain way matters for safety and trust.
Current explainable RL methods work with different types of data, from simple numerical inputs to complex images. XRL-Bench's support for both tabular and image-based state explanations means the benchmark can evaluate techniques across diverse real-world applications, from gaming to potentially more critical domains.
The paper introduces TabularSHAP as a new competitive explanation method and demonstrates its practical value in online gaming services. This shows that standardized benchmarking can drive innovation in XRL techniques and enable their deployment in actual commercial systems.
Open-source benchmarking infrastructure accelerates responsible AI development. By making XRL-Bench publicly available, the authors enable broader adoption of explainable RL practices across industry and academia, supporting the wider goal of making AI systems more transparent and trustworthy.

Abstract

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.

10 pages, 5 figures

Key Takeaways

The lack of standardized evaluation methods for explainable AI in reinforcement learning has been a significant barrier to progress. XRL-Bench addresses this gap by providing the first unified benchmark framework, enabling researchers and practitioners to fairly compare different explanation techniques on a level playing field.
State-explaining techniques are critical for real-world AI safety and accountability. These methods reveal which factors influence an AI agent's decisions at any given moment, which is essential in high-stakes applications where understanding "why" the AI acted a certain way matters for safety and trust.
Current explainable RL methods work with different types of data, from simple numerical inputs to complex images. XRL-Bench's support for both tabular and image-based state explanations means the benchmark can evaluate techniques across diverse real-world applications, from gaming to potentially more critical domains.
The paper introduces TabularSHAP as a new competitive explanation method and demonstrates its practical value in online gaming services. This shows that standardized benchmarking can drive innovation in XRL techniques and enable their deployment in actual commercial systems.
Open-source benchmarking infrastructure accelerates responsible AI development. By making XRL-Bench publicly available, the authors enable broader adoption of explainable RL practices across industry and academia, supporting the wider goal of making AI systems more transparent and trustworthy.

Abstract

10 pages, 5 figures

XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links