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Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

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arxiv 1903.09708 v2 pith:UFGCHURM submitted 2019-03-22 cs.HC cs.AI

Andrew Anderson , Jonathan Dodge , Amrita Sadarangani , Zoe Juozapaitis , Evan Newman , Jed Irvine , Souti Chattopadhyay , Alan Fern
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Margaret Burnett
This is my paper · ORCID
classification cs.HCcs.AI
keywords barsinvestigatelearningmentalreinforcementsaliencyachieveaddition
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We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.

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    A thesis presenting four methods (ASAF, TeamReg, CoachReg, constrained RL, goal-conditioned GFlowNets) that improve reward specification for deep RL through demonstrations, policy regularization, behavior constraints,...

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