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; Shuyan Liu; Zeyu Zhang

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arXiv · 2505.07854

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·Shuyan Liu·Zeyu Zhang

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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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Yufei Lin, Chengwei Ye, Huanzhen Zhang, Kangsheng Wang, Linuo Xu, Shuyan Liu, Zeyu Zhang. 2025-05-08. CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution. https://arxiv.org/abs/2505.07854

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