用强化学习探索因果关系，提升对系统变量的无测量估计精度
Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges
- 通过因果好奇机制在不直接测量下估计系统关键变量
- 首次分析该方法在机器人操控中的测量精度与局限性
- 揭示其对混淆因子的解耦能力，适合复杂系统优化研究
因果理解在科学与工程领域至关重要，旨在揭示系统中各因素如何因果影响实验或情境，并为构建或优化模型提供路径。应用场景包括自主探索未知环境或优化大型复杂系统中的关键变量。本文分析一种名为因果好奇（Causal Curiosity）的强化学习方法，该方法旨在不直接测量的情况下，尽可能准确高效地估计决定系统动态的因果变量。尽管该思路具有前景，但测量精度是方法有效性的基础。本文聚焦当前因果好奇在机器人机械臂上的应用，首次开展对其未来潜力与现有局限性的测量精度分析，并评估其敏感性及混淆因子解耦能力——这对因果分析至关重要。基于研究结果，本文提出改进设计建议，以推动因果好奇方法在真实复杂场景中的高效应用。
原文摘要 · Abstract (English)
Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or situation and pave a pathway towards creating effective or optimising existing models. Examples of use cases are autonomous exploration and modelling of unknown environments or assessing key variables in optimising large complex systems. In this paper, we analyse a Reinforcement Learning approach called Causal Curiosity, which aims to estimate as accurately and efficiently as possible, without directly measuring them, the value of factors that causally determine the dynamics of a system. Whilst the idea presents a pathway forward, measurement accuracy is the foundation of methodology effectiveness. Focusing on the current causal curiosity's robotic manipulator, we present for the first time a measurement accuracy analysis of the future potentials and current limitations of this technique and an analysis of its sensitivity and confounding factor disentanglement capability - crucial for causal analysis. As a result of our work, we promote proposals for an improved and efficient design of Causal Curiosity methods to be applied to real-world complex scenarios.
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