论文标题
无模型可解释的强化学习的远端解释
Distal Explanations for Model-free Explainable Reinforcement Learning
论文作者
论文摘要
在本文中，我们介绍并评估了一个无模型的强化学习代理的远端解释模型，该模型可以为“为什么”和“为什么不”问题产生解释。我们的出发点是，因果模型可以生成机会链，以“ a enbus b和b引起c”的形式。使用对人类代理实验中产生的240种解释的分析的见解，我们定义了远端解释模型，该模型可以使用决策树和因果模型来分析反事实和机会链。使用复发性的神经网络来学习机会链，并使用决策树来提高任务预测和生成的反事实的准确性。我们使用不同的强化学习算法在6个强化学习基准中计算评估该模型。从对90名人类参与者的研究中，我们表明，与两个基线解释模型相比，我们的远端解释模型在三种情况下的结果改善。
In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Our starting point is the observation that causal models can generate opportunity chains that take the form of `A enables B and B causes C'. Using insights from an analysis of 240 explanations generated in a human-agent experiment, we define a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. A recurrent neural network is employed to learn opportunity chains, and decision trees are used to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 reinforcement learning benchmarks using different reinforcement learning algorithms. From a study with 90 human participants, we show that our distal explanation model results in improved outcomes over three scenarios compared with two baseline explanation models.
