基于因果建模的强化学习控制: 现状及展望

孙悦雯; 柳文章; 孙长银

doi:10.16383/j.aas.c220823

基于因果建模的强化学习控制: 现状及展望

doi: 10.16383/j.aas.c220823

孙悦雯^1,,
柳文章^2,,
孙长银^{1, 3, 4,}

1.
东南大学自动化学院南京 210096
2.
安徽大学人工智能学院合肥 230601
3.
自主无人系统技术教育部工程研究中心合肥 230601
4.
安徽省无人系统与智能技术工程研究中心合肥 230601

基金项目: 国家自然科学基金(62236002, 61921004)资助

详细信息

作者简介:
孙悦雯：东南大学自动化学院博士研究生. 2017年获得山东大学学士学位. 主要研究方向为强化学习与因果发现. E-mail: amber_sun@seu.edu.cn

柳文章：安徽大学人工智能学院博士后. 2016年获得吉林大学学士学位, 2022年获得东南大学博士学位. 主要研究方向为多智能体强化学习, 迁移强化学习. E-mail: wzliu@ahu.edu.cn

孙长银：东南大学自动化学院教授. 主要研究方向为智能控制与优化, 强化学习, 神经网络, 数据驱动控制. 本文通信作者. E-mail: cysun@seu.edu.cn

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出版历程
- 收稿日期: 2022-10-18
- 录用日期: 2023-02-10
- 网络出版日期: 2023-02-20
- 刊出日期: 2023-03-20

Causality in Reinforcement Learning Control: The State of the Art and Prospects

SUN Yue-Wen^1
,,
LIU Wen-Zhang^2
,,
SUN Chang-Yin^{1, 3, 4
,}

1.
School of Automation, Southeast University, Nanjing 210096
2.
School of Artificial Intelligence, Anhui University, Hefei 230601
3.
Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei 230601
4.
Anhui Unmanned System and Intelligent Technology Engineering Research Center, Hefei 230601

Funds: Supported by National Natural Science Foundation of China (62236002, 61921004)

More Information

Author Bio:
SUN Yue-Wen　Ph.D. candidate at the School of Automation, Southeast University. She received her bachelor degree from Shandong University in 2017. Her research interest covers reinforcement learning and causal discovery

LIU Wen-Zhang　Postdoctor at School of Artificial Intelligence, Anhui University. He received his bachelor degree and Ph.D. degree from Jilin University in 2016 and Southeast University in 2022, respectively. His research interest covers multi-agent reinforcement learning and transfer reinforcement learning

SUN Chang-Yin　Professor at the School of Automation, Southeast University. His research interest covers intelligent control and optimization, reinforcement learning, neural networks, and data-driven control. Corresponding author of this paper

摘要

摘要: 基于因果建模的强化学习技术在智能控制领域越来越受欢迎. 因果技术可以挖掘控制系统中的结构性因果知识, 并提供了一个可解释的框架, 允许人为对系统进行干预并对反馈进行分析. 量化干预的效果使智能体能够在复杂的情况下 (例如存在混杂因子或非平稳环境) 评估策略的性能, 提升算法的泛化性. 本文旨在探讨基于因果建模的强化学习控制技术 (以下简称因果强化学习) 的最新进展, 阐明其与控制系统各个模块的联系. 首先介绍了强化学习的基本概念和经典算法, 并讨论强化学习算法在变量因果关系解释和迁移场景下策略泛化性方面存在的缺陷. 其次, 回顾了因果理论的研究方向, 主要包括因果效应估计和因果关系发现, 这些内容为解决强化学习的缺陷提供了可行方案. 接下来, 阐释了如何利用因果理论改善强化学习系统的控制与决策, 总结了因果强化学习的四类研究方向及进展, 并整理了实际应用场景. 最后, 对全文进行总结, 指出了因果强化学习的缺点和待解决问题, 并展望了未来的研究方向.
- 强化学习控制 /
- 因果发现 /
- 因果推理 /
- 迁移学习 /
- 表示学习
Abstract: Causality research has shown its potential and advantages in the reinforcement learning community. Beyond the inherent capability of inferring causal structure from data, causality provides an explainable toolset for investigating how a system would react to an intervention. Quantifying the effects of interventions allows actionable decisions to be made while maintaining robustness in the complex system (e.g., in the presence of confounders or under nonstationary environments). This paper explores how causality can be incorporated into different aspects of control systems and introduces recent advances in causal reinforcement learning. First, the concept and algorithms of reinforcement learning are introduced, and two main challenges, e.g., lack of causal explanation of observation variables and hard to transfer in transferable environments, are discussed. Second, the lines of research within causality are reviewed, including causal effect estimation and causal discovery, which provide potential solutions to address the aforementioned challenges. After that, how to embed causality in reinforcement learning systems is introduced. Four kinds of research advances in causal reinforcement learning are summarized and analyzed, followed by real-world applications. Finally, this paper summarizes and presents opening problems and future work prospects.
- Reinforcement learning control /
- causal discovery /
- causal inference /
- transfer learning /
- representation learning
注释:

1) ¹ 混杂因子指的是系统中两个变量未观测到的直接原因.

2) ² 马尔科夫等价类指的是满足相同条件独立性的一组因果结构.

3) ³ 遗憾值指的是实际算法的累计损失和理性算法的最小损失之间的差值.

HTML全文

图 1 强化学习框图

Fig. 1 The framework of reinforcement learning

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图 2 结构因果模型及其组成部分

Fig. 2 Structural causal model

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图 3 在倒立摆系统中提取系统变量之间的因果关系

Fig. 3 Causal representation in cart pole system

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图 4 因果技术在强化学习控制系统各环节的应用

Fig. 4 The application of causality in reinforcement learning control system

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图 5 MDP和POMDP的数据生成过程

Fig. 5 Data generation process in MDP and POMDP

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表 1 强化学习算法分类及其特点

Table 1 Classification of reinforcement learning algorithms

强化学习方法	具体分类	代表性模型	算法特点
	模型已知	AlphaZero^[24], ExIt^[25]	状态转移模型已知, 现实场景下不易实现
有模型强化学习	模型可学习: 结构化数据	PILCO^[29]	数据利用率高, 适用于低维状态空间
	模型可学习: 非结构化数据	E2C^[33], DSA^[34]	与机器学习相结合, 适用于高维冗余状态空间
	基于值函数的方法	SARSA^[37], 深度Q网络^{[36, 39]}	采样效率高, 但是无法实现连续控制
无模型强化学习	基于策略梯度的方法	PG^[44], TRPO^[45], PPO^[46]	对策略进行更新, 适用于连续或高维动作空间
	两者结合的方法	DDPG^[47], Actor-Critic^[48]	包含两个网络, 分别更新值函数和策略函数

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表 2 因果理论研究内容

Table 2 Classification of causality research

研究内容	具体分类	代表算法	算法特点
	没有混杂因子的干预类估计	回归调整^[60], 倾向得分方法^[61]	对样本采取适当的调整措施
因果效应估计	存在混杂因子的干预类估计	前门调整^[62], 后门调整^[62]	借助额外的假设进行估计
	反事实推理	标准三步骤^[63]	回答反事实问题
	基于条件约束的方法	PC^[64], FCI^[67]	基于条件独立性假设
因果关系发现	基于分数的方法	GES^[70], FGES^[71]	基于评分标准对因果图打分
	基于函数因果模型的方法	LiNGAM^[74], ANM^[75−76], PNL^[77−78]	需要对函数类型作出假设

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表 3 因果强化学习算法总结

Table 3 The classification of causal reinforcement learning algorithms

研究内容	代表算法	解决问题
因果表征提取	ASR^[83], CCPM^[84], MABUC^[88], B-kl-UCB^[89]	对高维冗余的原始数据进行因果结构化表征
环境因果模型	AdaRL^[95], CCRL^[97], IAEM^[98], OREO^[102]	在非平稳或异构环境中构建可迁移的环境因果模型
动作效果估计	CEHRL^[103], SDCI^[104], 倾向性评分^[109], FCB^[110]	量化智能体动作对于环境的影响, 获得数据的无偏估计
反事实动作推理	CF-GPS^[111], 反事实数据增强^[81]	提高算法的样本效率和可解释性

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