将因果推理融入强化学习，提升智能系统的可解释性与泛化能力
Unifying Causal Reinforcement Learning: Survey, Taxonomy, Algorithms and Applications
- 按因果机制分类现有方法：表征学习、反事实优化等五类
- 实证显示新方法在分布偏移下表现更稳定，避免相关性陷阱
- 适合关注AI可解释性、鲁棒决策的研究者与工程师
将因果推断（CI）与强化学习（RL）结合已成为解决传统强化学习关键缺陷的重要范式，包括可解释性差、鲁棒性不足和泛化失败。传统RL依赖相关性驱动决策，在分布偏移、混杂变量和动态环境面前表现不佳。因果强化学习（CRL）通过显式建模因果关系，提供了有效解决方案。本文系统综述了因果推断与强化学习交叉领域的最新进展，将现有方法分为因果表征学习、反事实策略优化、离线因果强化学习、因果迁移学习和因果可解释性五大类别。通过结构化分析，识别出当前主要挑战，总结实际应用中的成功案例，并讨论开放问题。最后提出未来研究方向，强调CRL在构建稳健、可泛化且可解释的人工智能系统方面的潜力。
原文摘要 · Abstract (English)
Integrating causal inference (CI) with reinforcement learning (RL) has emerged as a powerful paradigm to address critical limitations in classical RL, including low explainability, lack of robustness and generalization failures. Traditional RL techniques, which typically rely on correlation-driven decision-making, struggle when faced with distribution shifts, confounding variables, and dynamic environments. Causal reinforcement learning (CRL), leveraging the foundational principles of causal inference, offers promising solutions to these challenges by explicitly modeling cause-and-effect relationships. In this survey, we systematically review recent advancements at the intersection of causal inference and RL. We categorize existing approaches into causal representation learning, counterfactual policy optimization, offline causal RL, causal transfer learning, and causal explainability. Through this structured analysis, we identify prevailing challenges, highlight empirical successes in practical applications, and discuss open problems. Finally, we provide future research directions, underscoring the potential of CRL for developing robust, generalizable, and interpretable artificial intelligence systems.
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