# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 6 Conclusions  
This study investigates the challenges faced by traditional DRL algorithms in dynamic path planning, particularly how confounding factors in the environment can impair convergence and generalization, making it difficult to generate high-quality paths. We propose a causal state-masking-based DRL method (CSM-TD3). Initially, we integrate a causal consistency loss term and dynamic state masking to combine causal inference mechanisms with TD3, compelling the policy network to rely solely on state features that reflect true causal relationships for decision making. This effectively filters out irrelevant features that do not contribute to the decision-making process. To enable TD3 to adaptively adjust the causal features across various state dimensions, we introduce an encoding configuration optimized through backpropagation learning. This allows the model to dynamically fine-tune causal features, enhancing the agent's convergence during training and its generalization ability across different environments. Numerical simulations validate the efficacy of the proposed method. The experimental results indicate a 25% improvement in convergence performance. The CSM-TD3 algorithm effectively addresses dynamic path-planning challenges across various complex environments. Compared to other DRL algorithms, our approach generates shorter paths with better smoothness and demonstrates superior obstacle-avoidance capabilities.  
Although the CSM-TD3 method demonstrates advantages in dynamic path planning, its training process relies on a large amount of training data and significant computational resources, which may limit its application in resource-constrained scenarios. Future research could integrate other forms of causal inference and adaptive mechanisms to make the learning strategy more efficient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DRL Algorithm** & **DQN** & **TD3** & **CSM-TD3** \\ \hline Location of Goal Point & & & \\ \((0,4)\) & 26 & 24 & 23 \\ \((-4,3)\) & 40 & 35 & 33 \\ \((4,4)\) & 67 & 50 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of the path steps obtained from the DQN, TD3, and the proposed CSM-TD3 when the obstacle is stationary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DRL Algorithm** & **DQN** & **TD3** & **CSM-TD3** \\ \hline Location of Goal Point & & & \\ \((0,4)\) & 49 & 42 & 23 \\ \((-4,3)\) & 44 & 34 & 32 \\ \((4,4)\) & 58 & 40 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the path steps obtained from the DQN, TD3, and the proposed CSM-TD3 when the obstacle is moving.  
**Author Contributions:** Conceptualization, [PERSON] and T.Z.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON], T.Z. and X.H.; formal analysis, [PERSON]; resources, [PERSON]; data curation, T.Z.; writing--original draft preparation, J.C. and T.Z.; writing--review and editing, [PERSON], J.C., T.Z. and J.C.; project administration, X.H.; funding acquisition, [PERSON] and T.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China through Grant No. 52005443, and the Natural Science Foundation of Zhejiang Province through Grant No. LQ21E050016.  
**Data Availability Statement:** The authors do not have permission to share data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.