Task Complexity Determines the Effectiveness of PPO-Based Improvements in Reinforcement Learning: An Empirical and Analytical Study

Ruoyu Wu

doi:10.54254/2755-2721/2026.GU33941

Article Information

Received: 12 May 2026

Published: 1 June 2026

DOI: https://doi.org/10.54254/2755-2721/2026.GU33941

Article Type: Research Article

Cite this article

Wu,R. (2026). Task Complexity Determines the Effectiveness of PPO-Based Improvements in Reinforcement Learning: An Empirical and Analytical Study. Applied and Computational Engineering,242,122-127.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: ACE Vol.242

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

Copyright & License

[1]J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal Policy Optimization Algorithms, " arXiv preprint, arXiv: 1707.06347, 2017.

[2]Sha, S., Liu, Y., and Lei, B., "Dynamic Proximal Policy Optimization: Enhancing PPO with Adaptive Entropy and Smooth Clipping, " Neurocomputing, vol. 674, pp. 132861–132861, 2026.

[3]Wang, H., "Improved PPO Algorithm Based on Adaptive Penalty Mechanism, " 2026.

[4]Su, B., "Research on Multi-Agent Reinforcement Learning and PPO-Based Improvements, " 2026.

[5]Khatami, A. H., "CMA-MAPPO: Integrating Covariance Matrix Adaptation Evolution Strategy with Multi-Agent Proximal Policy Optimization for Enhanced Exploration in Sparse-Reward Environments, " Swarm and Evolutionary Computation, vol. 102, pp. 102330–102330, 2026.

[6]C. Jiaju, Z. Chai, Z. Jue, Y. Hao, Y. Zheng, and D. Zhao, "A Survey of Cooperative Multi-Agent Reinforcement Learning for Multi-Task Scenarios, " Artificial Intelligence Science and Engineering, 2025.

[7]Y. Wang, H. He, and X. Tan, "Truly Proximal Policy Optimization, " in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.

[8]J. Dai, J. Ji, L. Yang, Q. Zheng, and G. Pan, "Augmented Proximal Policy Optimization for Safe Reinforcement Learning, " in Proceedings of the AAAI Conference on Artificial Intelligence, 2023.

[9]C. Yu, A. Velu, E. Vinitsky, S. Gao, Y. Wang, A. Bayen, and Y. Wu, "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games, " in Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 24611–24624, 2022.

[10]K. Cobbe, O. Klimov, C. Hesse, T. Kim, and J. Schulman, "Quantifying Generalization in Reinforcement Learning, " in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.

[11]E. Brunskill and L. Li, "Sample Complexity of Multi-Task Reinforcement Learning, " arXiv preprint arXiv: 1309.6821, 2013.