RACCER：面向强化学习的可达且确定的反事实解释
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
AI总结：
RACCER是首个面向强化学习的反事实解释方法，通过定义RL特有属性并采用启发式树搜索，生成可达且结果确定的反事实，实验和用户研究证明其优于现有方法。
摘要：
尽管强化学习算法已成功应用于众多任务，但其对神经网络的依赖使其行为难以理解和信任。反事实解释是一种人性化的解释，为用户提供可操作的建议，以改变模型输入，从而从黑盒系统中获得期望的输出。然而，当前在强化学习中生成反事实的方法忽略了强化学习任务的随机性和顺序性，可能产生难以获得或无法实现预期结果的反事实。在这项工作中，我们提出了RACCER，这是第一个针对强化学习的、为强化学习智能体行为生成反事实解释的方法。我们首先提出并实现了一组强化学习特有的反事实属性，以确保反事实易于达到且预期结果具有高概率。我们使用智能体执行轨迹的启发式树搜索，基于所定义的属性找到最合适的反事实。我们在两个任务中评估了RACCER，并进行了一项用户研究，以表明与当前最先进的方法相比，强化学习特有的反事实能帮助用户更好地理解智能体的行为。
英文摘要：
While reinforcement learning (RL) algorithms have been successfully applied to numerous tasks, their reliance on neural networks makes their behavior difficult to understand and trust. Counterfactual explanations are human-friendly explanations that offer users actionable advice on how to alter the model inputs to achieve the desired output from a black-box system. However, current approaches to generating counterfactuals in RL ignore the stochastic and sequential nature of RL tasks and can produce counterfactuals that are difficult to obtain or do not deliver the desired outcome. In this work, we propose RACCER, the first RL-specific approach to generating counterfactual explanations for the behavior of RL agents. We first propose and implement a set of RL-specific counterfactual properties that ensure easily reachable counterfactuals with highly probable desired outcomes. We use a heuristic tree search of the agent's execution trajectories to find the most suitable counterfactuals based on the defined properties. We evaluate RACCER in two tasks as well as conduct a user study to show that RL-specific counterfactuals help users better understand agents' behavior compared to the current state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
