Although multi-agent reinforcement learning (MARL) has achieved significant success in various domains, its deployment in real-world scenarios remains challenging, particularly in communication-constrained environments involving multi-task coupling. Existing methods suffer from two limitations: (1) the inability to effectively integrate and process incomplete state from disparate agents, and (2) a lack of robust mechanisms for handling complex multi-task coupling. To address these challenges, we propose the Coupled Communication-Task Decoupling (CCTD) framework. CCTD introduces two critical innovations: first, a distributed state compensation mechanism to process historical data, thereby reconstructing accurate global states from partial observations; second, a hierarchical architecture that systematically decomposes complex tasks into manageable subtasks while preserving their interdependencies. Thanks to its modular design, CCTD can integrate with existing MARL algorithms and allow for flexible combination of various subtasks. Extensive experiments demonstrate that CCTD outperforms baseline methods, achieving a 10% improvement in communication reception rate and superior performance across all subtasks in multi-task environments.
Li et al. (Thu,) studied this question.