Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

@article{Remman2022CausalVM,
  title={Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning},
  author={Sindre Benjamin Remman and Inga Str{\"u}mke and Anastasios M. Lekkas},
  journal={2022 American Control Conference (ACC)},
  year={2022},
  pages={2683-2690}
}
We investigate the effect of including application knowledge about a robotic system states’ causal relations when generating explanations of deep neural network policies. To this end, we compare two methods from explainable artificial intelligence, KernelSHAP, and causal SHAP, on a deep neural network trained using deep reinforcement learning on the task of controlling a lever using a robotic manipulator. A primary disadvantage of KernelSHAP is that its explanations represent only the features… 

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