Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is a counterfactual, which deals with "what if?" scenarios. In this work, we introduce the concept of a counterfactual state to help humans gain a better understanding of what would need to change (minimally) in an Atari game image for the agent to choose a different action. We introduce a novel method to create counterfactual states from a generative deep learning architecture. In addition, we evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our user study results suggest that our generated counterfactual states are useful in helping non-expert participants gain a better understanding of an agent's decision making process.

研究人员提出了一个新的概念 - 对抗状态来帮助人类更好地理解深度强化学习决策的逻辑，通过对生成对抗网络的改进，创建对抗状态并在大众中进行用户测试，结果表明这种方法有助于非机器学习专家更好地理解代理的决策过程。

通过生成式深度学习获得 Atari 游戏智能体的反事实状态