🤖 AI Summary
In multi-agent reinforcement learning (MAS) under sparse rewards, training inefficiency and policy fragility arise from delayed feedback and difficulty in sharing experience across agents. To address these challenges, this paper proposes a collaborative curriculum learning framework. Its key contributions are: (1) a multidimensional curriculum design jointly modulating task difficulty, agent count, and environmental complexity; (2) a variational evolutionary algorithm for automated subtask generation; and (3) a co-evolutionary mechanism integrating agent policy optimization with environmental model learning. The framework unifies curriculum learning, variational evolution, MAS, and environment modeling. Evaluated on five cooperative benchmarks—including MPE and Hide-and-Seek—our method achieves significant improvements over state-of-the-art approaches: 2.1× faster convergence on average and an 18.7% increase in success rate, demonstrating both effectiveness and generalizability.
📝 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.