让强化学习决策可解释，通过假设情景分析行为原因
Explaining Reinforcement Learning Decisions in Self-adaptive Systems
- 用反事实情景对比，解释智能体在自适应系统中的决策逻辑
- 在真实自行车共享系统模拟中验证，能有效揭示策略行为动因
- 适合需要信任与可解释性的自动化系统开发者与研究者
强化学习（RL）广泛应用于自主与自适应系统，但深度强化学习依赖神经网络，决策过程缺乏透明度，难以理解，导致用户信任度下降，系统验证也更困难。为此，本文提出一种名为EARL的Python库，用于生成强化学习环境中的反事实解释。该工具允许用户通过探索‘如果…会怎样’的情景，对比不同可能结果，从而澄清智能体行为。反事实解释在心理学研究中被证明直观易懂，但在强化学习领域仍处于早期阶段，现有实现多局限于简单示例。EARL支持在真实强化学习驱动的自适应系统中生成解释。为验证其适用性，本文在CitiBikes自适应共享单车系统仿真中展示了应用效果，并提供了实际场景下的评估结果。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explanations by exploring What-if scenarios to clarify agent behavior by comparing possible outcomes. Counterfactual explanations have been shown to be intuitive and user-friendly in psychology research, but have only recently been explored in RL, with existing implementations usually limited to toy examples and benchmarks. EARL supports counterfactual explanation generation in realistic RL-based self-adaptive systems. To demonstrate its applicability, we demonstrate its use in a simulation of CitiBikes, a self-adaptive bike-sharing system, and we provide evaluations showing how it performs in real applications.
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