Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling

ArXi:2605.12312v1 Announce Type: cross 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.