Enabling Symbiosis in Multi-Robot Systems Through Multi-Agent Reinforcement Learning

Current cyber-physical systems, including multi-robot systems, often fail to interoperate effectively, resulting in suboptimal performance, inefficient resource utilization, and poor resilience. Inspired by natural symbiotic relationships, such as tree-fungi networks, we propose an architecture that integrates ecological symbiosis principles into multi-robot system specifications. Specifically, we incorporate symbiotic principles into multiagent reinforcement learning (MARL) within a centralized training, decentralized execution framework. Comprehensive scenario-based evaluations in a simulated warehouse environment show that our symbiotic MARL framework improves system performance ($10.7\%$) and resource utilization ($13.81\%$) compared to non-symbiotic baselines. Agents dynamically adjust their behavior in response to environmental changes, ensuring continuous task execution, efficient navigation, and balanced energy use. These findings demonstrate that integrating ecological principles into MARL enhances the system's efficiency and performance. The framework's success in promoting sustainable resource usage while maintaining high task performance suggests broader applications across various cyber-physical domains where adaptive coordination is crucial.