# Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
## 4 Conclusions  
Our experimental results in multiple classic reinforcement learning environments, particularly Atari 2600 games, revealed several key findings. First, a seven-layer network architecture demonstrated the best overall performance, balancing feature extraction and policy learning capabilities. This architecture neither suffered from insufficient depth to capture complex patterns nor from overfitting and training difficulties associated with excessive depth. Second, deeper networks exhibited higher risks of overfitting and instability, especially in rapidly changing or complex game environments. In contrast, shallower networks struggled to handle complex dynamics and strategic adjustments required by certain games. We also conducted robustness tests by introducing external perturbations such as image blurring. The results indicated that the seven-layer network maintained the best performance under these conditions, suggesting its superior robustness. This can be attributed to the appropriate balance in network depth, allowing effective feature extraction without overfitting to noise.  
Furthermore, our analysis of network architecture design highlighted the importance of adaptive configurations based on task complexity. The proposed adaptive deep Q-Dagger network architecture, which dynamically adjusts depth according to factors like state space size, reward sparsity, and action space size, proved effective in enhancing model flexibility and performance across diverse tasks. In summary, this study provides comprehensive guidance for designing and optimizing regenerative reinforcement learning models. By thoroughly exploring the relationship between network depth and model robustness, our findings establish a theoretical foundation for developing more efficient and stable reinforcement learning models. Although the experimental results show that the proposed architecture exhibits high robustness and excellent performance in multiple Atari 2600 games, the verification of this study mainly focuses on a small number of classic environments and does not fully explore other influencing factors such as network width, activation function, and regularization strategy. Therefore, the generalization ability of this method still needs to be further verified in larger-scale, more complex, or diverse task environments.  
The authors confirm contribution to the paper as follows: study conception and design: [PERSON] and [PERSON]; data collection: [PERSON], X.C., Y.X., J.H., K.W. and J.J.; analysis and interpretation of results: [PERSON] and Z.W; draft manuscript preparation: [PERSON] and Z.W. All authors have read and agreed to the published version of the manuscript.  
This work is jointly supported by the National Natural Science Foundation of China (No. 62372173, No. 52078212, No. 62476086, No. 61300132), the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, and China Institute of Water Resources and Hydropower Research, China (No.IWHR-SKL-202003).  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.