Minghui Geng

In hazardous material transportation route planning, complex environments, stringent safety requirements, and sparse rewards often cause traditional reinforcement learning methods to converge slowly and yield unstable policies. To address these issues, this paper proposes TAR-RL (Trend-Aware and Attention-Augmented Reward Redistribution Reinforcement Learning), which integrates reward redistribution and adaptive exploration to improve the efficiency and robustness of path planning. Specifically, a dynamic ε-greedy strategy based on Q-value trends is introduced to adaptively adjust the exploration rate. Meanwhile, the RUDDER framework decomposes and redistributes delayed rewards to strengthen learning signals at critical decision points. Furthermore, a multi-head attention mechanism is incorporated to model long-term dependencies between states and actions, improving return prediction accuracy. An intrinsic reward mechanism encourages proactive exploration and autonomous learning. Experiments on two grid environments with different complexity levels demonstrate that TAR-RL achieves faster convergence, higher success rates, and more stable training dynamics than baseline methods. These results indicate that TAR-RL is well-suited for safety-aware path planning in high-risk routing scenarios.