AINeutralarXiv – CS AI · 7h ago6/10
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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
Researchers propose CL-MARL, a curriculum learning framework for multi-agent reinforcement learning that dynamically adjusts task difficulty based on agent performance, addressing a fundamental limitation where fixed-difficulty training constrains policy generalization. The method achieves 40% win rate on complex cooperative tasks, outperforming existing baselines by significant margins.