Modelling Agent Policies with Interpretable Imitation Learning

• URL: http://arxiv.org/abs/2006.11309v1
• Date: Fri, 19 Jun 2020 18:19:08 GMT
• Title: Modelling Agent Policies with Interpretable Imitation Learning
• Authors: Tom Bewley, Jonathan Lawry, Arthur Richards
• Abstract summary: We outline an approach to imitation learning for reverse-engineering black box agent policies in MDP environments. We explicitly model and learn agents' latent state representations by selecting from a large space of candidate features constructed from the Markov state.
• Score: 12.858982225307809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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