Assessing Explainability in Reinforcement Learning

Abstract

Reinforcement Learning performs well in many different application domains and is starting to receive greater authority and trust from its users. But most people are unfamiliar with how AIs make their decisions and many of them feel anxious about AI decision-making. A result of this is that AI methods suffer from trust issues and this hinders the full-scale adoption of them. In this paper we determine what the main application domains of Reinforcement Learning are, and to what extent research in those domains has explored explainability. This paper reviews examples of the most active application domains for Reinforcement Learning and suggest some guidelines to assess the importance of explainability for these applications. We present some key factors that should be included in evaluating these applications and show how these work with the examples found. By using these assessment criteria to evaluate the explainability needs for Reinforcement Learning, the research field can be guided to increasing transparency and trust through explanations.