We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld.

中文翻译：

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迈向使用认知架构的深度强化学习代理的心理学

我们认为，认知模型可以在人类用户和深度强化学习 (Deep RL) 算法之间提供一个共同点，以实现可解释的人工智能 (AI)。将人类和学习者都作为认知模型提供了比较和理解其潜在决策过程的通用机制。这种共同基础使我们能够识别差异并以人类可以理解的术语解释学习者的行为。我们展示了突出每个模型决策中最相关特征的新颖显着性技术，以及该技术在星际争霸 II 和 OpenAI 网格世界等常见训练环境中的示例。