Attribution-Guided Active Exploration in Deep Reinforcement Learning for Autonomous Driving Decision-Making

Deep reinforcement learning often suffers from inefficient exploration, which is commonly addressed by introducing an auxiliary model that assigns intrinsic rewards when the agent encounters novel scenarios. However, such approaches increase training complexity and computational overhead. This paper proposes an Attribution-Guided Reinforcement Learning (AGRL) framework that exploits real-time attribution analysis to guide exploration in autonomous driving decision-making. The proposed method is built upon the Kolmogorov–Arnold-Network-based Interpretable Deep Reinforcement Learning (KAN-IDRL) framework. Specifically, action-wise attribution patterns are computed online, and perturbations are applied to the state inputs to measure attribution sensitivity. The resulting attribution-sensitivity signal identifies actions whose decision rationales are more locally responsive to state changes, and these actions are therefore preferentially explored. In addition, local attribution results collected from a pretrained interpretable policy are aggregated into global feature-importance scores, which are then used to initialize a trainable prior attention gate in a Prior-Attention-Enhanced Kolmogorov–Arnold Network (PAE-KAN). This design allows the policy to incorporate attribution-derived prior knowledge while maintaining sufficient adaptability for task-specific learning. Experiments across multiple autonomous driving scenarios demonstrate that the proposed AGRL framework achieves faster convergence and competitive final performance compared with representative baseline methods. These findings indicate that attribution information can be transformed from a post hoc interpretability tool into an effective guidance signal for improving reinforcement learning.