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

• URL: http://arxiv.org/abs/2402.04869v1
• Date: Wed, 7 Feb 2024 14:09:34 GMT
• Title: Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
• Authors: Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao
• Abstract summary: We consider explicitly modeling the generation process of states with the graphical causal model. We formulate the causal structure updating into the RL interaction process with active intervention learning of the environment.
• Score: 40.33036146207819
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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