Explainability In Deep Reinforcement Learning, A Review Into Current Methods And Applications

Abstract

The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since their first introduction in 2015. Though uses in many different applications are being found, they still have a problem with the lack of interpretability. This has bread a lack of understanding and trust in the use of DRL solutions from researchers and the general public. To solve this problem, the field of Explainable Artificial Intelligence (XAI) has emerged. This entails a variety of different methods that look to open the DRL black boxes, ranging from the use of interpretable symbolic Decision Trees (DT) to numerical methods like Shapley Values. This review looks at which methods are being used and for which applications. This is done to identify which models are the best suited to each application or if a method is being underutilised.

Stats

• citations43
• S2 citations—
• github stars0
• HF likes0
• heat score12.33
• arxiv keyhickling2022explainability

Related papers

• Explainability In Deep Reinforcement Learning (2020)0.00
• A Survey On Explainable Reinforcement Learning: Concepts, Algorithms, Challenges (2022)0.00
• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)15.80
• Utilizing Explainability Techniques For Reinforcement Learning Model Assurance (2023)2.16
• A Survey Of Explainable Reinforcement Learning: Targets, Methods And Needs (2025)0.00
• Domain-level Explainability -- A Challenge For Creating Trust In Superhuman AI Strategies (2020)0.00
• Explainable Reinforcement Learning: A Survey (2020)0.00
• Explainable Artificial Intelligence (XAI) For Increasing User Trust In Deep Reinforcement Learning Driven Autonomous Systems (2021)0.00