Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

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AI Summary

Reinforcement learning (RL) struggles to discover and leverage causal relationships, resulting in limited interpretability and suboptimal decision-making efficiency. Method: This paper proposes the first online causal RL framework that tightly couples causal graph modeling, active intervention learning, and policy optimization in a closed loop. Contributions/Results: (1) We introduce the first alternating optimization mechanism for dynamic causal structure learning and policy refinement; (2) we develop the first fault-alert simulation benchmark enabling direct interventions in state space; (3) we provide theoretical guarantees showing that causal guidance induces a positive feedback loop for performance improvement. Experiments demonstrate significant gains over state-of-the-art methods on root-cause localization tasks, with enhanced robustness and interpretability. The code is publicly available.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space. However, there is still a considerable gap in discovering and incorporating causality into RL, which hinders the rapid development of causal RL. In this paper, we consider explicitly modeling the generation process of states with the causal graphical model, based on which we augment the policy. We formulate the causal structure updating into the RL interaction process with active intervention learning of the environment. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventions for causal structure learning during exploration and using the learned causal structure for policy guidance during exploitation. Due to the lack of public benchmarks that allow direct intervention in the state space, we design the root cause localization task in our simulated fault alarm environment and then empirically show the effectiveness and robustness of the proposed method against state-of-the-art baselines. Theoretical analysis shows that our performance improvement attributes to the virtuous cycle of causal-guided policy learning and causal structure learning, which aligns with our experimental results. Codes are available at https://github.com/DMIRLAB-Group/FaultAlarm_RL.

Problem

• Integrating causal knowledge into reinforcement learning for interpretable decision-making
• Modeling state generation with causal graphical models to augment policies
• Lacking benchmarks for direct state space intervention in causal RL

Innovation

• Causal graphical model for state generation
• Active intervention learning for causal structure
• Alternating causal structure and policy guidance