Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization - researchr publication references

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Jinwei Xing, Takashi Nagata, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar. Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization. Neural Networks, 161:228-241, April 2023. [doi]

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