CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution

Yufei Lin, Chengwei Ye, Huanzhen Zhang, Kangsheng Wang, Linuo Xu +2 more
5/8/2025
cs.AIcs.MA

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Abstract

Sparse reward environments pose significant challenges in reinforcement learning, especially within multi-agent systems (MAS) where feedback is delayed and shared across agents, leading to suboptimal learning. We propose Collaborative Multi-dimensional Course Learning (CCL), a novel curriculum learning framework that addresses this by (1) refining intermediate tasks for individual agents, (2) using a variational evolutionary algorithm to generate informative subtasks, and (3) co-evolving agents with their environment to enhance training stability. Experiments on five cooperative tasks in the MPE and Hide-and-Seek environments show that CCL outperforms existing methods in sparse reward settings.

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Cite this paper

@article{lin2025ccl,
  title  = {CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution},
  author = {Yufei Lin and Chengwei Ye and Huanzhen Zhang and Kangsheng Wang and Linuo Xu and Shuyan Liu and Zeyu Zhang},
  year   = {2025},
  eprint = {2505.07854},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.07854v1}
}

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