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Jan, 2024
BET: 通过错误决策解释深度强化学习
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
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Xiao Liu, Jie Zhao, Wubing Chen, Mao Tan, Yongxing Su
TL;DR
提出了一种名为Backbone Extract Tree(BET)的全新的自解释结构,可以更好地解释代理的行为,识别易出错的状态,并且在各种流行的强化学习环境下显示出其对现有自解释模型的优越性能。
Abstract
Despite the impressive capabilities of
deep reinforcement learning
(DRL) agents in many challenging scenarios, their black-box decision-making process significantly limits their deployment in safety-sensitive domains. Several previous
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