As we deploy autonomous agents in safety-critical domains, it becomes important to develop an understanding of their internal mechanisms and representations. We outline an approach to imitation learning for reverse-engineering black box agent policies in MDP environments, yielding simplified, interpretable models in the form of decision trees. As part of this process, we explicitly model and learn agents’ latent state representations by selecting from a large space of candidate features constructed from the Markov state. We present initial promising results from an implementation in a multi-agent traffic environment.
Original languageEnglish
Publication statusAccepted/In press - 4 Sept 2020
Event1st TAILOR Workshop at ECAI 2020 - Santiago de Compostela, Spain
Duration: 4 Sept 20205 Sept 2020


Workshop1st TAILOR Workshop at ECAI 2020
Abbreviated titleTAILOR 2020
CitySantiago de Compostela
Internet address


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