解释自适应系统中强化学习的决策
Explaining Reinforcement Learning Decisions in Self-adaptive Systems
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中文总结 AI 辅助
针对强化学习策略透明度不足的问题，本文提出EARL库用于生成反事实解释，并在CitiBikes模拟中验证了其在实际自适应系统中的适用性。
中文摘要 AI 辅助
强化学习（RL）已被广泛应用于自主系统和自*系统，但RL策略，尤其是依赖神经网络的深度RL策略，缺乏透明度且难以理解。这会降低用户信任，也让系统验证更具挑战性。为应对这一挑战，本文推出了用于强化学习的替代现实解释库（EARL），这是一个用于在RL场景中生成反事实解释的Python库。该库允许用户通过探索“如果”场景生成解释，通过比较可能的结果来阐明智能体行为。反事实解释在心理学研究中被证明直观且用户友好，但在RL中仅在近期才被探索，现有实现通常局限于玩具示例和基准。EARL支持在基于RL的现实自适应系统中生成反事实解释。为展示其适用性，我们在自适应共享单车系统CitiBikes的模拟中演示了其使用，并提供了显示其在实际应用中性能的评估结果。
英文摘要
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.
