Learning Partial Action Replacement in Offline MARL

Learning Partial Action Replacement in Offline MARL explores A new framework for offline multi-agent reinforcement learning that adaptively selects partial action replacements to significantly improve computational efficiency and performance on complex benchmarks.. Commercial viability score: 7/10 in Offline MARL.