Multi-Agent Reinforcement Learning (MARL) has evolved from independent learning to sophisticated communication-based coordination systems like Adaptive Topology Communication (ATC). However, current communication-based approaches face limitations in domain knowledge integration, adaptation speed, and learning paradigm flexibility that hinder real-world deployment. This study proposes three synergistic MARL framework optimization techniques—Physics-Informed MARL, Meta-Learning MARL, and Hybrid Learning MARL—that extend beyond communication-based coordination to address these fundamental limitations. These methods integrate domain-specific knowledge, enable rapid adaptation to new scenarios, and provide flexible learning paradigms that dynamically balance centralized and decentralized approaches. Experimental evaluation in warehouse coordination tasks demonstrates statistically significant reward improvements of 2.4%–31.3% over state-of-the-art baselines (ATC), with Hybrid Learning MARL achieving the highest performance. The results validate that proposed techniques can substantially enhance communication-based MARL systems while maintaining safety and coordination effectiveness for practical deployment.