Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis

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@Article{Zhang:2020:CIS,

• author = "Hengzhe Zhang and Aimin Zhou and Xin Lin",
• title = "Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis",
• journal = "Complex \& Intelligent Systems",
• year = "2020",
• volume = "6",
• pages = "741--753",
• keywords = "genetic algorithms, genetic programming, Reinforcement learning, Policy derivation, Explainable machine learning, XAI",
• DOI = "doi:10.1007/s40747-020-00175-y",
• size = "13 pages",
• abstract = "Reinforcement learning based on the deep neural network has attracted much attention and has been widely used in real-world applications. However, the black-box property limits its usage from applying in high-stake areas, such as manufacture and healthcare. To deal with this problem, some researchers resort to the interpretable control policy generation algorithm. The basic idea is to use an interpretable model, such as tree-based genetic programming, to extract policy from other black box modes, such as neural networks. Following this idea, we try yet another form of the genetic programming technique, evolutionary feature synthesis, to extract control policy from the neural network. We also propose an evolutionary method to optimize the operator set of the control policy for each specific problem automatically. Moreover, a policy simplification strategy is also introduced. We conduct experiments on four reinforcement learning environments. The experiment results reveal that evolutionary feature synthesis can achieve better performance than tree-based genetic programming to extract policy from the neural network with comparable interpretability.",
• notes = "Shanghai Key Laboratory of Multidimensional information Processing, School of Computer Science and Technology, East China Normal University, Shanghai, China",

}

Genetic Programming entries for Hengzhe Zhang Aimin Zhou Xin Lin

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