Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling

• URL: http://arxiv.org/abs/2605.12312v1
• Date: Tue, 12 May 2026 15:58:58 GMT
• Title: Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
• Abstract summary: In cross-task scenarios, changes in task objectives and reward formulations reduce the reusability of previously acquired task knowledge.<n>This paper proposes a transferable delay-aware reinforcement learning method based on implicit causal graph modeling.
• Score: 11.943568201005931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true propagation process of action effects. In cross-task scenarios, changes in task objectives and reward formulations further reduce the reusability of previously acquired task knowledge. To address this problem, this paper proposes a transferable delay-aware reinforcement learning method based on implicit causal graph modeling. The proposed method uses a field-node encoder to represent high-dimensional observations as latent states with node-level semantics, and employs a message-passing mechanism to characterize dynamic causal dependencies among nodes, thereby learning transferable structured representations and environment dynamics knowledge. On this basis, imagination-driven behavior learning and planning are incorporated to optimize policies in the latent space, enabling cross-task knowledge transfer and rapid adaptation. Experimental results show that the proposed method outperforms baseline methods on DMC continuous control tasks with random delays. Cross-task transfer experiments further demonstrate that the learned structured representations and dynamics knowledge can be effectively transferred to new tasks and significantly accelerate policy adaptation.

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