@inproceedings{1e23a45d50f04f25abb85f4066de9e31,
title = "A Model Compression Method for Deep Reinforcement Learning",
abstract = "Deep reinforcement learning (DRL) achieves remarkable success in complex tasks, but its large model size makes it a major challenge to deploy it on resource-constrained platforms while maintaining high performance and achieving high compression ratio. In this paper, we propose a DRL model compression method that combines group regularization pruning and random sketches. We first verify the effectiveness in a classic DRL environment and then demonstrate its application value in a multi-agent confrontation environment. Experimental results show that our method achieves a high compression ratio and superior policy performance over baselines.",
keywords = "Deep reinforcement learning, Group regularization pruning, Multi-agent systems, Random sketches",
author = "Xin Deng and Lei Chen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 9th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2025 ; Conference date: 31-10-2025 Through 03-11-2025",
year = "2026",
doi = "10.1007/978-981-95-8329-4\_15",
language = "English",
isbn = "9789819583287",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "174--185",
editor = "Yongzhao Hua and Yishi Liu and Rui Yan",
booktitle = "Proceedings of 2025 9th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Optimization Technologies",
address = "Germany",
}