Journal article

Explainable reinforcement learning through a causal lens

P Madumal, T Miller, L Sonenberg, F Vetere

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence | Association for the Advancement of Artificial Intelligence (AAAI) | Published : 2020

Abstract

Prominent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen by referring to counterfactuals — things that did not happen. In this paper, we use causal models to derive causal explanations of the behaviour of model-free reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generate explanations of behavi..

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