Generating Explanations Based on Markov Decision Processes

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Reinforcement Learning in Healthcare: A Survey (2019, background): “generating explicit explanations for sequential decision making problems [334].”
A Natural Language Argumentation Interface for Explanation Generation in Markov Decision Processes (2011, background): “Other work has focused on finding the variable which is most influential to determining the optimal action at the current state [5], while using an extensive knowledge-base to translate these results into natural language explanations.”
On Learning Sparse Boolean Formulae for Explaining AI Decisions (2017, background): “Specific approaches have been introduced to discover explanations in specific domains such as MDPs [9], HTNs [14] and Bayesian networks [36].”